{
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  "metadata": {
    "colab": {
      "name": "IIT-ISM_Intro to ML for PE.ipynb",
      "provenance": [],
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      "authorship_tag": "ABX9TyMCbRndOqsghPloxWQuCbJa"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "peBM_EWxY3Xt"
      },
      "source": [
        "# Intoduction to Machine Learning for Petroleum Engineers. \n",
        "\n",
        "> Divyanshu Vyas | Oil & Gas Data Scientist. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MeME17XFiO-J"
      },
      "source": [
        "### Agenda : \n",
        "\n",
        "1. Python : Lists, Dictionaries, Loops, Functions. \n",
        "\n",
        "2. Data Analysis : NumPy & Pandas. \n",
        "\n",
        "3. Data Visualization : Matplotlib\n",
        "\n",
        "4. A Brief Talk on Machine Learning Algorithms. \n",
        "\n",
        "5. Hands on Excercises :-\n",
        "\n",
        "> A. Hands on Practice : DCA with Python\n",
        "\n",
        "> B. Hands on Practice : Machine Learning a Popular $ \\phi - K $ correlation. \n",
        "\n",
        "> C. Hands on Practice : Machine Learning based Artificial Lift Selection. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kAsubkOjlNhJ"
      },
      "source": [
        "# 1. Python Lists\n",
        "\n",
        "> Mutable: You can index and change elements. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5M0t7qskYx17"
      },
      "source": [
        "#The Following are different types of lists. \n",
        "\n",
        "#list of strings. \n",
        "litho_names = ['SST' , 'LST' , 'SH' , 'DOLO']\n",
        "\n",
        "#list of porosities\n",
        "porosities = [0.25 , 0.13 , 0.38 , 0.17]\n",
        "\n",
        "#list of permeabilities. \n",
        "perms_md = [100 , 2 , 0.5 , 3.5 ]"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "irZM_USGr8vv"
      },
      "source": [
        "Accessing the Data in Lists. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BmaxrnW4r4mg",
        "outputId": "cb40905a-5033-433d-bcdd-a1a489075f58"
      },
      "source": [
        "numbers = [1,2,4,5,6,7,8,12,12,13,45,56,200]\n",
        "\n",
        "len(numbers)"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "13"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7Qa6q9heuXv-",
        "outputId": "3dfa3d0f-879d-4713-a15c-0ba86e3188e8"
      },
      "source": [
        "numbers[0] , numbers[1] , numbers[2] , numbers[3] #... and so on"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1, 2, 4, 5)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SZAfkYlKuwe9"
      },
      "source": [
        "The folliwing is called List Slicing. listname[start:st0p:step]"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fJFNqVIludpc",
        "outputId": "6b572eb5-a9ce-49af-de04-4032a106bf7b"
      },
      "source": [
        "numbers[0:5]"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[1, 2, 4, 5, 6]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mny4h0Ykulcf"
      },
      "source": [
        "#List reversal\n",
        "\n",
        "original = [1,2,3,4,5,6]\n",
        "\n",
        "reversed_list = original[-1::-1]"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lwQMNEU0wNnU",
        "outputId": "136a84d8-ee0b-4adc-c933-a4db6d42c097"
      },
      "source": [
        "reversed_list"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[6, 5, 4, 3, 2, 1]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M_eiFejgw5r_"
      },
      "source": [
        "# 2. Python Dictionaries\n",
        "\n",
        "{key : value}"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "D0xDcOq3wPbf",
        "outputId": "214f3373-f37b-42f2-ca3e-56fede70ab62"
      },
      "source": [
        "rock_details = {'sst1': 0.25 , 'sst2': 0.30 , 'lst1': 0.12 , 'lst2': 16}\n",
        "\n",
        "rock_details"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'lst1': 0.12, 'lst2': 16, 'sst1': 0.25, 'sst2': 0.3}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5PuDoH4SxL3u",
        "outputId": "2afa68d3-b3b2-4d57-e09e-c0ed3898e816"
      },
      "source": [
        "reservoir_data = {'porosities': [0.24, 0.25 , 0.34 , 0.44] , \n",
        "                  'perms_md' : [120, 100, 60 , 500]}\n",
        "\n",
        "reservoir_data['porosities']"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.24, 0.25, 0.34, 0.44]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MJzJ-oqkx0ae"
      },
      "source": [
        "# 3. Python : Loops & Iterations."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KUtGHfFHxx3M",
        "outputId": "62d24dda-6566-4fde-eeb1-b5e694816de9"
      },
      "source": [
        "porosities"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.25, 0.13, 0.38, 0.17]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VFB71dc5x5ny",
        "outputId": "187a5f56-58c7-4b20-b602-c3cd726ab888"
      },
      "source": [
        "for i in porosities:\n",
        "  print(i)"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.25\n",
            "0.13\n",
            "0.38\n",
            "0.17\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9_kySONgx8_U",
        "outputId": "95a6652f-7933-41ea-9d49-f45bbea44fb1"
      },
      "source": [
        "litho_names"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['SST', 'LST', 'SH', 'DOLO']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "C3uHAT_ayAca",
        "outputId": "b84a82d3-dbfb-4558-ad80-9416a4492a53"
      },
      "source": [
        "for i in range(len(litho_names)):\n",
        "\n",
        "  print(f'Name{i+1} is {litho_names[i]}')"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Name1 is SST\n",
            "Name2 is LST\n",
            "Name3 is SH\n",
            "Name4 is DOLO\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xIIJZZz9yMIH",
        "outputId": "58e1c687-7c76-4d8b-b514-6970fc5a6d31"
      },
      "source": [
        "for i in range(10):\n",
        "  print(i)"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0\n",
            "1\n",
            "2\n",
            "3\n",
            "4\n",
            "5\n",
            "6\n",
            "7\n",
            "8\n",
            "9\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ub4Z7EiiyTbx",
        "outputId": "654996bf-94ed-4f84-9158-d691d936c28a"
      },
      "source": [
        "mylist = []\n",
        "for i in range(100):\n",
        "  mylist.append(i)\n",
        "\n",
        "print(mylist)"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GMvW2c2iygLc"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UKwvFdSny1SP"
      },
      "source": [
        "# 4. Python Functions - VVVVIMP!"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CujiDasCy0b4"
      },
      "source": [
        "def funcname( parameter1 , parameter2):\n",
        "\n",
        "  res = parameter1 + parameter2\n",
        "\n",
        "  return res"
      ],
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HUu4a9slzDtW",
        "outputId": "11f34a7c-8685-4cd7-c1de-204d2c2f972a"
      },
      "source": [
        "funcname(1,2)"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bcEisEP0zJzt"
      },
      "source": [
        "# Suppose we have a function f(x) = -x^2 + 5x + sinx + 5 \n",
        "# We want to create a function that returns y for each x according to this mapping. \n",
        "\n",
        "def f(x):\n",
        "\n",
        "  import math\n",
        "\n",
        "  y = -x**2 + 5*x + math.sin(x) + 5\n",
        "\n",
        "  return y\n",
        "\n"
      ],
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hEvnGRAH4klm",
        "outputId": "1e00d62f-34b8-4a8f-8e23-d8117babc0dd"
      },
      "source": [
        "f(3)"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "11.141120008059868"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "FA94j8aM4lrE",
        "outputId": "2ec1f998-2e65-49f6-c1d4-8e8a9d3880e3"
      },
      "source": [
        "#Optional. What if we are interested in seeing the graph of this function. \n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "X = [1,2,3,4,5,6,7,8,9,10]\n",
        "\n",
        "Y= []\n",
        "\n",
        "for i in X:\n",
        "\n",
        "  Y.append(f(i))\n",
        "\n",
        "plt.plot(X,Y)"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f66ffd2a150>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WthaEBo645TI"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eB-ZJ0Km7VYB"
      },
      "source": [
        "# 5. Data Analysis (NumPy and Pandas) & Visualization (Matplotlib)\n",
        "\n",
        "> NumPy : Numerical Python : Built on C++. Superfast. \"NumPy Arrays\"\n",
        "\n",
        "> Pandas : Excel of Python : Built on Top of NumPy. \"Pandas DataFrame\"."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6qCd6vy0L_Ws"
      },
      "source": [
        "## 1. NumPy"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JuU4bmck7wtG"
      },
      "source": [
        "import numpy as np"
      ],
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BgmWJOtKLZJd",
        "outputId": "d9493bac-2f23-423c-f576-e6b9857eefb7"
      },
      "source": [
        "num1 = [1,2,3,4,5]\n",
        "num2 = [5,6,7,8,9]\n",
        "\n",
        "#Lets first Directly convert the list to NumPy arrays -> use np.array(listname)\n",
        "arr1 = np.array(num1)\n",
        "arr2 = np.array(num2)\n",
        "\n",
        "#And Let's now solve the List Disadvantages using NumPy\n",
        "print(arr1)\n",
        "print(arr2)\n",
        "print(arr1*arr2)\n",
        "print(arr1 + arr2)"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[1 2 3 4 5]\n",
            "[5 6 7 8 9]\n",
            "[ 5 12 21 32 45]\n",
            "[ 6  8 10 12 14]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "J-0b1DRZHrLY",
        "outputId": "1a930f34-506a-414e-a712-661d1f48a205"
      },
      "source": [
        "#linspace creates number of points between a start and an end point.\n",
        "Sw = np.linspace(0,1,50)\n",
        "\n",
        "krw = Sw**3\n",
        "\n",
        "kro = (1-Sw)**3\n",
        "\n",
        "#plotting\n",
        "plt.plot(Sw,krw,label='Water Relperm')\n",
        "plt.plot(Sw,kro,label='Oil Relperm')\n",
        "plt.grid()\n",
        "plt.legend()"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f66ff623b10>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xjKOsySmKTxP",
        "outputId": "38928657-5b34-4990-9e7c-674dc5175cd3"
      },
      "source": [
        "Sw"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.        , 0.02040816, 0.04081633, 0.06122449, 0.08163265,\n",
              "       0.10204082, 0.12244898, 0.14285714, 0.16326531, 0.18367347,\n",
              "       0.20408163, 0.2244898 , 0.24489796, 0.26530612, 0.28571429,\n",
              "       0.30612245, 0.32653061, 0.34693878, 0.36734694, 0.3877551 ,\n",
              "       0.40816327, 0.42857143, 0.44897959, 0.46938776, 0.48979592,\n",
              "       0.51020408, 0.53061224, 0.55102041, 0.57142857, 0.59183673,\n",
              "       0.6122449 , 0.63265306, 0.65306122, 0.67346939, 0.69387755,\n",
              "       0.71428571, 0.73469388, 0.75510204, 0.7755102 , 0.79591837,\n",
              "       0.81632653, 0.83673469, 0.85714286, 0.87755102, 0.89795918,\n",
              "       0.91836735, 0.93877551, 0.95918367, 0.97959184, 1.        ])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Mu33orqDK_vi"
      },
      "source": [
        "Suppose you want to create an array x where each value is center of a grid block."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MjvN5wUHK8Ds",
        "outputId": "fca40176-dfe8-4177-c1f3-d25889f7787c"
      },
      "source": [
        "dx = 10 #ft\n",
        "\n",
        "np.arange(0,110,dx)"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([  0,  10,  20,  30,  40,  50,  60,  70,  80,  90, 100])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Mcc-W1K8LMpa",
        "outputId": "ac72e8e8-b2d5-4e26-d688-456c507e861d"
      },
      "source": [
        "\n",
        "#Will be useful for Reservoir simulation. Space-Time Arrays.\n",
        "\n",
        "#Suppose you want to build an array for days. from t = 0th day to t= 500th day. \n",
        "#Time step = 20 days. \n",
        "\n",
        "t = np.arange(0,520,20)\n",
        "\n",
        "t"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([  0,  20,  40,  60,  80, 100, 120, 140, 160, 180, 200, 220, 240,\n",
              "       260, 280, 300, 320, 340, 360, 380, 400, 420, 440, 460, 480, 500])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tW0IpkaSLn4N"
      },
      "source": [
        "Universal Functions Make Scientific computations easy using NumPy : https://www.w3schools.com/python/numpy_ufunc.asp"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ww616-nrLixm",
        "outputId": "d0780ae9-50f5-41d0-86d4-ba6740faf058"
      },
      "source": [
        "np.log2(t)"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:1: RuntimeWarning: divide by zero encountered in log2\n",
            "  \"\"\"Entry point for launching an IPython kernel.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([      -inf, 4.32192809, 5.32192809, 5.9068906 , 6.32192809,\n",
              "       6.64385619, 6.9068906 , 7.12928302, 7.32192809, 7.4918531 ,\n",
              "       7.64385619, 7.78135971, 7.9068906 , 8.02236781, 8.12928302,\n",
              "       8.22881869, 8.32192809, 8.40939094, 8.4918531 , 8.56985561,\n",
              "       8.64385619, 8.71424552, 8.78135971, 8.84549005, 8.9068906 ,\n",
              "       8.96578428])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ieqTwTSaLz0S",
        "outputId": "82768405-7cc3-4c0a-e0be-fc09ae872f77"
      },
      "source": [
        "np.sin(t)"
      ],
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 0.        ,  0.91294525,  0.74511316, -0.30481062, -0.99388865,\n",
              "       -0.50636564,  0.58061118,  0.98023966,  0.21942526, -0.80115264,\n",
              "       -0.8732973 ,  0.08839871,  0.94544515,  0.6832397 , -0.38780942,\n",
              "       -0.99975584, -0.42815543,  0.65031074,  0.95891572,  0.13232187,\n",
              "       -0.85091936, -0.82681172,  0.17610529,  0.97054255,  0.61601671,\n",
              "       -0.46777181])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "YbZCF2YhL4AU",
        "outputId": "faf36b5c-dc87-4f2d-cb25-8ff3d0c1007f"
      },
      "source": [
        "plt.plot(t,np.sin(t))"
      ],
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f66ff5cdb90>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 51
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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LOLTER6CUelgpdZ1Saq9S6hPmto8qpR4qOebfK6XuL3vfT5VSR5RSN5l/v2jFeCphsAJBAJBsjTOaqo6EqgsT85y7Osftq9QWWovetjj1kVBVaQRDOnR0kxoBwP/+i3vJ5Jf48s8u2TAyZ1koLPH3/zzEWw530VFSlXcj7N5WfZFD58fn2NOxvn+glHfdlGQ0nePpy9M2jWprBJnFZVzS5aebNzfRNclEvGpMQ7rIXHkTmmsRDgl7OqsrckjbtsvLT2+EA93N3HZwOw/87KLva1I98vwY05lF7n71xpzEpezpaOTiRPWUH5lbKDA2s3axubW4/VAXsbqQ58xDgSAoQ0cMbVTdK6c3Easa09Cjp8c41NNC3wYS60rZX2UhpBcnDC1xKxoBwNtv6GZqPu/7efHgk5fpa4u/oinRRujvaGR2ocDEXHWYyC6M64ihzc2JxvoIbz7YxcPPjXqqm18gCMoYnMpsyVGs6UnEmckVfN+gZHo+z/GLU7xlE2Yhzb7tTQynsmTy/v4NNBcn52muj7ysA9VmWIkm86/J8OLEPD89P8ndr97xih4dG6HfDLGsFvPQwIQuNrc50xAYpakn5vI8MeCdzPNAEJSglNFqcSuhoxp90Y/6fPX3gzNXKarNmYU0OnKoWqJELpoN67eqJfa0GtFko2n/zokHnxokHBJ+7diO9Q9ehWqrQnr+6hwh2Vg9snLeeGA7TfURT5mHAkFQwuR8nsw65afXo7dKQkgfOz1GV0s9NyRf2XVqPaothPTS5HxlWmKruThI+1cjeGJgkmO72uhqWb0XxXokE3Gi4RAXJqqjL8H5iXl2tDdQH9lYGZpSYnVh3nq4i++dGvVME6NAEJSgm2dUIgiqwQywUFjiR2fGefOhri2ZAXZvayQckqoQBItLRYamX9mwfjPEo2HaGup8vTgYSWUrui7CIWHntoaq0QiMPsWbNwtp3nVTkplcgZ+ctbdczkYJBEEJlYaOAmxvjhEOia/NAE8MTDGfX+ItG8wmLicaCbGrvaEqBMHQdJalolq36uh69LTGfWsuzBeKjM8t0GMucrZKtVQhNYrNzS2bu7bC6/Z1kGio4yGPmIcCQVCCTiarxEcQDgndLTFfr/4ee2GMeF2Y1+7dtuXP2Lu9qSoa2ev6OJtNJisnmYj71jR0dTaHUpBs3ZpZSLOno5GLkxnPJVNtFqMH88aKza1FNBLibYe7eeyFMU+E1AaCoITLUxm6WuqJ1W3e7ldK0schpEopHjs9xr+4rqOi32Hf9iYuTsz7PnZ+JYegMo3Az3NCC7BKNYLdHY3kC0VGfKwtw0oQxGZDR8s51NPMfH6JSQ9knQeCoIStVB1djZ7WuG99BM+PzDCazm24yNxa7OtsolBUvm9afnFinqb6jdeTWYueViOseN6HYcVagFWqEfQvRw752zykq+tWohHAimD1QiWCQBCUcHmystBRTTIR50o650sV+NEXxggJ3HZw8/kDpVRL5JAROrr1BEONLkjoR9+RVRrBnioRBAPjRknyjqbKFgdJM5rMCxpSIAhMdPlpKzSC3kSM/FKRiXn/Nag5OZTiQHcL2zZZS6Yc3b/Y770JLq3TsH6j6BBSP2qKo6kszbEITfWRij6ns7mexmjY9/klAxNz7OnceLG5teg2NawrHvAdBYLAZGjakMqVxItr/BxCeiWdo7fClR8YTct7WmO+1ggWl4oMbqBh/UbQSWV+9BOMpHPLq9dKEBF2V0HkkBE6WvniYFtjlGg4FGgEXsKK0FHNyurP/X/wZhlJZZfNGJXi97aVwzp01AKNoLs1hohxU/Ubo+ksPRbNif6ORl83qJlfKDCazlWUQ6AJhYTu1ljgI/ASVoSOanp92qlsfqHATK6wrLJWyt5Oo22lH30lABcsihgCqAuH2N5c78tcgtFUblmjqZQ9HY0MTmU8k1G7WbQ2U0kOQSk9rTFP+I0CQWByaTJDrC5EZ4W2cYCWeITGaNh3piHtFLTCDACGRpDJLzE646/fQXNpOYfAqovef7kEuUUjvLHHojnR39lIUa0svPyG9nlpH1ilJBPeiDC0RBCIyB0ickZEzonI/avs/x0RGReRE+bj/SX77hWRs+bjXivGsxUqLT9dioiY/2D3Jf1m0CsTq1Z/fo8cujiZoTEarjg6RJNMxDxhD94M2pFp1Zzo7/B3FdLz4/OExBpfIhgmw7EZ9yMMKxYEIhIGPgu8HTgM3CMih1c59KtKqaPm4wvme9uBjwG3ArcAHxORtkrHtBUGpzLsbLdm5QdGqJ3fLnptq7Rq9ed/QWBEDFmxOABdZiLniUzSjaLncNKCAAKAflO7uuhTQTAwPkdf29aKza1GsjVGoaiYmHM3wtAKjeAW4JxSakAplQceBO7a4HvfBjyqlJpSSk0DjwJ3WDCmTaHLT1vhKNYYDWrcV/k2g77ou1orN4+BERWRaKjzrSC4NJlZToKygp7WGNnFJdLZRcs+025WFgfWaAStDXW0N0YZ8K0gmN90V7JrsRxY4rLJ0ApB0AsMlrweMreV86si8qyIfENEdFHzjb4XEblPRI6LyPHxcWsr9k3M5ckuLrGz3ZpVDxh29om5BXKLS5Z9pt1cSefoaKq3bLUjIuzrbOK8DwVBYalYcZOicvSq2k91qFbMhdZdG0bxOf/NiWJRMTAxZ0nEkEZHY7kdROCUs/jbwG6l1I0Yq/4HNvsBSqnPK6WOKaWOdXZ2Wjq4y1PG6mQrTSbWQmdheiFZZKOMpHOWhY5q9vm0+NxwKkvBgqqjpSw3qPGRpjiSztHWUEc8as3iAPxbhXR0JmcWm7NuTiSrSCMYBkrbFvWZ25ZRSk0qpbQR7AvAL2z0vU5w2cIcAo2+ofrJTzCaytK9xcYja7FvexNT83kmXbaBbpYLFkcMwUpYsRfCBTfKaCprqTYAhiAYm1nwXd2lAV1jqMM6jSDRUEesLsQVl+eEFYLgKWC/iPSLSBS4G3io9AAR6Sl5eSdw2nz+CPBWEWkzncRvNbc5yuVJ45+w2Sbt16LXh9nFV9I5y5yCmr0+dRjrYnm7O6ybEx1N9dSFxfXV32YYtUFL1H4XvyWWLVcd3W7d4kBEjCKVftcIlFIF4IMYN/DTwNeUUs+LyMdF5E7zsA+JyPMichL4EPA75nungP+AIUyeAj5ubnOUy1MZultiFZefLqXbZyUFZnOLzC4ULHMKavaZ9lS/mYcuTMzTEA1bkleiCYWErpaY6/bgzTBik0YA/gshPT8+R3N9xNI5AWZSmctzorIqUiZKqYeBh8u2fbTk+UeAj6zx3i8BX7JiHFtl0OKIIYD6SJiOpnrfCAKd6GRVVrGmNxEnXhf2oUZgbeioJumB1d9G0ZnmVpWX0Ghz2wWfFZ/TEUNWz4me1jg/PT9h6WduliCzGLg0NW9JaYlyehMx31z0yzXnLTYNhULCns5GHwqCDP0WmoU0PQlvlBTYCHqcVmWaa+LRMD2tMd9pBAPj1kYMaZIJI6ms4GITp5oXBLnFJcZmFizXCABfZRdbnUFayr7t/gohLSwVuTyVsaT8dDk9rf7pVTFicQ5BKf0djcu1nPxAJl9gJJ2zNGJI09Map6hg3MWAipoXBEPThlPQynhxjRYEfsgkHUnnEIEui6OGwPATjKRzvokSGUnlKBTVchaslfQmYiwuuZ9JuhH04sBqLRH8F0KqHcWVdiVbjZUS5e5ZD2peEFhZdbScntYYmbw/MklHU1k6m+qpC1s/Jfb5rEmNXqnasTjwSibpRhhJZ21bHPR3NJLKLDLtgX69G0FnQtuiEXige10gCCatzyHQ+CmE9MpMruJWhGvht5pDl0xBYGV5CY1XMkk3wmjKyDSPRqy/Tegbql9KTQyMzyFibV6JRi8O3Ew0DATBVJZ4nXUVJktJ+qgvwUgqW3Fz8rXYta2RcEh8IwguTMwTrwvT2WxtmCB4J5N0I4yk7ZsTu31WfO78+Dx9bXFLQ8w1LTGzbH2gEbjH5al5y8pPl9Pjk+xipRSj6Zzl8eKaaCTE7m0NvhEElyaNGkN2zAmdSeoLjcDGObGjvYFwSHzjJxgYn7M0o7gUEaEnEQ80Aje5PJWxtMZQKR2N9UTDIc8XGZvJFsjkl2yJDtH4qebQxcl5W8xCYPaq8EGDGqWUUV7C4hwCTV04xM72Bl8IgmJRmX2K7REEYCaVudjAqaYFgR3lp0sJhcSIG/e4j2B0xqwwadNFD4YguDTp/RaFK1VH7REEYPzOnl8c5ArM27w46O9o9IWP4MpMjuziki2OYo3b2cU1LQjG5xbILRZtEwRgZpJ6/KK3uiHNauzb3sRSUS07Yr3KaDrH4pKyJZlMY2gEHp8TNpSfLmf3tkYuTsx7Prx6JXTUTkEQZ3xuwbWFUk0LgkEbqo6W05OIeV4QjFjconI19nU2A96PHNKmCns1gjhXZxdYdDGTdD304sDqgnOl9Hc2kjUTOr3MgNk7YZ+NpqFkIoZSMOaSeaimBYGuMGlHDoGmNxHnisvp4+sxmsoREthuQ5SMRlds9LogsDN0VJNsdfei3wgjDmgEezp0CKm358T5q3M01UdsiSLTLIeQuuQ7qmlBcHkqgwj0tdk32ZMJI3386qx3Vz2j6RxdLTEiNiSTaRqiEXoTcc87jC9MZIjVhWwVij0Jdy/6jeDE4sAvVUgHJuwpNlfKctMil0yGNS8IrC4/XY4fcglG01lbzUKavdubfKER7Lah6mgpSR+UKB9JZ21fHBjXXsjzVUgHxueXtRe7cHtxUNOCYHAqY6tZCFYuei9HidgZL17Kvs4mzo/Pebrg2kVTENiJ2xf9RhhN5WxfHIRCYjiMPRxAkM0vMZzK2ho6CtBUH6E5FnEtcqimBYGdoaMar1/0RjKZMxrBvu1N5BaLnhWKS0XF4FSWXTZGDMHKRe9ljWA0nbWt5EgpXg8h1f4LO4rNleNmrwpLBIGI3CEiZ0TknIjcv8r+D4vICyLyrIg8LiK7SvYticgJ8/FQ+Xvtws7y06U01Udojdd59qJPZRbJLRYdueiXaw551E8wksqSXyraUnW0nN5E3LM1qHSmuV3lJUrp72jk8mTGs8EUToSOatzsVVGxIBCRMPBZ4O3AYeAeETlcdtgzwDGl1I3AN4D/VLIvq5Q6aj7uxCF06KgdFSbL8XJfgpHl5iPOaASAZ3sTXJy0P3RU09Pq3QY1U/N5FgpFR8yF/R2NFIqKoWlv/hYD4/OI2BtFpulpjS2X/nYaKzSCW4BzSqkBpVQeeBC4q/QApdQPlFIZ8+UTQJ8F31sRdpafLifZGmPYo6s/HS9udYvK1WhvjNLeGPWsw/iiDQ3r16In4d0yE6Np+3MINMuRQx71E5wfn6M3YU+xuXJ6WuNMzOVZKCzZ/l3lWCEIeoHBktdD5ra1eB/wvZLXMRE5LiJPiMi713qTiNxnHnd8fHy8shGzIgjsNg2BoRF4dfWn65vY0XxkNfZ1NnHWq4JgYp5YXYiuZvtvgMnWGFPzeXKLzl/066G1V6c0AvBu/+KBiTlH/AOwEkLqhlbgqLNYRH4LOAb855LNu5RSx4DfBD4jIntXe69S6vNKqWNKqWOdnZ0Vj+XyVIaGaJhtjdaXny4nmYiTyix6skPXaCpLJCR0NNkXL16KDiH1YlmBS5Pz7GpvJBSyL3RU43YC0bXQY7Kz9pSmvTFKSyziyVwCpRQXHAgd1SRd7F9ihSAYBnaUvO4zt70MEbkd+GPgTqXUcnaVUmrY/DsA/BC42YIxrcvlyYxt5afLSXqgA9Fa6GSysAM3PzD8BOnsIhNz3utMdXEy44hZCLzdoGYknaUuLHQ02r84EBH6O5s8KQjGZhaYzy+xd7uzGoEb9wkrBMFTwH4R6ReRKHA38LLoHxG5GfgchhC4WrK9TUTqzecdwOuAFywY07o4ETqq0ZLei34Cp0JHNTr6wmux40tFxeXJjO05BBrdoMaLobRX0jm6W2OOaEYA/du8WY5at1bd65BG4KaWWLEgUEoVgA8CjwCnga8ppZ4XkY+LiI4C+s9AE/D1sjDRQ8BxETkJ/AD4pFLKdkFgd/npcrQg8OLqbzRtX4vK1ejzaKb1aNoIHd3t0EXfvbz68+DiIOVMgqGmv6OJkXTWc7Yj4b0AACAASURBVP6SgXHncggA4tEwiYY6VzSCiBUfopR6GHi4bNtHS57fvsb7fgocsWIMm2F8doGFQtG2hjTldDXXExLv3fx0vPgd1zunEaxoR976LS5OOBdODBAz26N60Vw4ks5ybFebY9/X39mIUkYRyAPdzY5973qcH5+nMRqmq8UZ/xkYWoEb/UtqMrPYydBRgEg4RFeL90JIJ+fz5AtFR0JHNY31ERINdQx7LG5cm6qcMg2BcdF7LamsWFSMzTirJe5ZLj7nrWiy8+NGxJATfkRNsjXmSnZxTQoCXX7aKdMQeDOpTIepOWkGACOr1nsawTz1kRDdLc4JRS8mlU3MLbC4pBxJMNTsXhYEmXWOdBY7W5auhVvZxTUpCJwoP12OF3MJtGByInGoFC8KxYtmw3qnHKRgzgmPaQQjLiwOdK1/L2kEhaUio6kcO9qdXST1tBqh5tm8s/6SmhQEg1MZelpi1EfszxbUJBOGyuelypvaUemkaQhMjWA666lcgksOVB0tp6c1xuxCgdncoqPfey10QIPTc6K/o9FTkUNjswsUiorehHNWA3AvhLQmBcFlB8pPl5NsjZMvFJmc9078/Gg651i8eCm9iTjz+SVmst5IsCsWFZemMo5FDGmSHqxMO5J2NtNcs8djgkD7sJy0GoB7IaQ1Kwic9A9A6UXvHZPIaDrraLy4pte8uIZS3rAJj87kyBeKjmsE2iTnJX/JaCpLfSREW0Odo9/b39HIxFyeGY9oR8Pm3Ox1WBDoOeG06bTmBEE2v8TVWfvLT5fj1j/4WjgdL67p1SGkHokcujihI4acNgPo/BLvaASj6RzJRNzRSBlYCdzQVYHdRs/NXoc1I7fyS2pOEAxOmxFDDl/0K5mk3rnoRxzOKtZ4rX3ncvlph01D2838Ei9piW7Nib4243r0Sjnq4VSWjqaoI1VHS6mP6PySQBDYymUXQkcBEg11xOvCnrn5LceLu6ARdDRFiUZCnjGJXJyYJxoJ0eNg6Cis5Jd4KZfALS1R2+K9IgiGprOOawOabhfCimtPEDhYfroUESHpYgeicibmzXhxh0NHwfgtvNSh6+Jkhl3tzoaOaryUS1BYKnJ1NufKnEg01NEYDXvGXDicyjruH9C4kV1ck4KgMRqm3YHy0+UkE3HPmIb0RHNj9QeG7XXIIxrB5cmMI13JVsNLDWrGZhcoKnfmhIjQ2xZnaNp9H4FSimEXNQIjuzjQCGxl0AwdddoZBmZzao/c/JZrzrtgDwbds9f930IpxeB0xvHEIY3+HbyQU6FzCJzoQ7AafW0NnjANGV3Ciq4Jgp5EnNlcgTkH+5fUnCC4NJVxrLBYOclE3Cx4536VRW2OcEsQ6N/C7YqT05lFMvmlZWel0/S0xlgoFJnyQH7Jcg6BS1pin0c0Au27cnNOAFxxUCuoKUFQLCoGXcgh0Gjb61h6YZ0j7Wc0nSMaCbliIoOV+Gy3zSI6XHGHi/ZgcP93AC9oBHFmcgXXcwmWQ0ddnhNO+tBqShCMz5nlp10SBL0eKsE8kjLCBN0wkYF38iqGpt1d/XnldwBDGDXVR2iJOZtMptHlHNx2GLuVTKZxo8xETQkCp8tPl9Pjofj5K+mca2YhgD6PXPTaFNHnko/ASxqBXhy4hVdCSIenszTH3BOI3a0xRHyoEYjIHSJyRkTOicj9q+yvF5Gvmvt/LiK7S/Z9xNx+RkTeZsV41sKtHAKNvsi8IAhG0znXbMGwMtnd1o4GpzO0xutcu+i3NUaJhkOOR4mshtPd6spZEQTu+gmGU+5FDAHUhUN0NtX7SyMQkTDwWeDtwGHgHhE5XHbY+4BppdQ+4NPAp8z3HsbocXw9cAfwX83Ps4VLy+Wn3REEuiuVG40nSlkqKq7M5FyzBQNEIyG2N9e7LgiGprOOFxYrJRQSI4HIA2HFxuLAvTnR3hglXud+LoHbcwKcDyu2QiO4BTinlBpQSuWBB4G7yo65C3jAfP4N4M1iGKfvAh5USi0opS4A58zPs4XBqQzJ1jjRiHsWsR4PhJCOzy6wVFR0u6gRwEo5ajcZnMqww6WFgcYLiYYLhSUm5hZcyyuB0lwCt30E7moEAD0tMd8Jgl5gsOT1kLlt1WPMZvdpYNsG3wuAiNwnIsdF5Pj4+PiWBhoNh7iht2VL77WKZCLmuiDQNx03V39gNqhx8QaolPLE6i/pgZaVOpLNTS0RzBBSF6vSprOLzOYKrjmKNT2JGKMO5pf4xlmslPq8UuqYUupYZ2fnlj7jU++5kc/9y2MWj2xzJD2QQDTqUovKcnrbjFR6t5r16Cgyt4IHND2JGFdmciy52LRoZHlx4O6c6HNZIxh2OYpMk2w1e3bknEkqs0IQDAM7Sl73mdtWPUZEIkArMLnB91YVy01ZHPoHr4bWSNyMEAHjt8gvFZmYcyevYsil5iPl9LTGWSoqxmfdyy9ZTjB0XSNoIJVZdDSrthTts3LdNJRwNoTUCkHwFLBfRPpFJIrh/H2o7JiHgHvN5+8Bvq+MJfFDwN1mVFE/sB940oIxeZaVZBH3Vj1X0jlidSESDjcfKUdfbG7VHBp0OZxYs5xL4KKZbCTlbskRjdu9Koan3c0h0DgdVlyxIDBt/h8EHgFOA19TSj0vIh8XkTvNw74IbBORc8CHgfvN9z4PfA14AfhH4ANKKffrL9iIFxKIdOioW8lkGn2xuXXRD7nUfKQcLzSoGU1naY3X0RCNuDYGcD+EdDiVJVYXYptLGfea5aQyh+aEJf91pdTDwMNl2z5a8jwH/Noa7/0E8AkrxuEH9E3HzRDSkXTWdRMAuN+gZmg6w7bGKI317t78ksurPxcXByl3Eww1bjeoGU5lXenQVo7TTYt84yyuFjqa6qkLi7saQSpHd4u7q2CAllgdzbGIa7kEXogYAmiJR2iMhl2NHBoxW1S6TUdTlHoXmxa5WX66FKebFgWCwGF0ApFbgsDN5iOr4WY56qHpLH0u+wfAiJ/vcbks96hLLSrLcbsvwXDKG4sDcLZpUSAIXMDNvgRXXWw+shq9CXfCBYtFo/lILV705WTzS6Qyi57QCMC9vgTZ/BITc3lPaARgZBdf8YuzOGDzuNmm0SthgpreNneE4tXZBfJLRdezijXJ1rhrfqMRl3tTlONWLoHbfQjK0Z3KnMg5CgSBC7iZQOR2Z7JykgmjBv2swzXoB3XVUa9oBIkYE3ML5AtFx7/b7bal5fS1xZmaz5PJO5tLsJxD4JE50d0aJ7dYJJWx/9oIBIELJBNGAtHVWedXgF676N3q0aBt0G7nEGiSrXGUgrEZ5+fEclaxV7REl3IJhj0STqzRJWCcyC8JBIELuBk2OZLO0hgN0xJzN2RS49ZvMTjlrYu+x8X8Er046PaIluhWCOlwKkMkJHS1eON30CXBncglCASBCyznErjgJxhN5cxeAO7GSWv6XEoqG5rO0NlcT6zOtqrnm0ILRDca1Iyms2bYpjd+ix0uJZUNT2fpbo0RDnnj2tAawagDWmIgCFzAzQY1ozPeiBfXdJp5FcMOC8XBqaxrfYpXQyeVuRE/P5LOecZUCEauTTQScrz0iBfKT5fS0VRPJCTLvaTtJBAELtBsJlK5YwbwRry4JhQSelrjzvsIUhnPRIcAxKNhEg11roSQenFOuBFWPDSd9YyjGIzfocuhvgSBIHCJ3oTz4YL5QpFxl5uPrIbRoMY5M0BhqchIKscOl/oUr0VPa9yVekOjHskqLsXpENLFpSJjMzn6PPY7ONW/JBAELpF0IZP06mwOpbwTOqpJOpxXoUN3vaQRgI4bd1YQzOSMks9emxN9bc4uDq6kcxSVd0JHNT2tzrSsDASBS/S4UGZiOYfAY6ue3rY4Y7M5x2LodcSQV5LJND0utKy84tE50dfWwMRcntyiM8WIhzzSkKacnkTMEFI25xwFgsAlkok405lFsnnnqm5rweN2i8py+hLOxtAPeSyZTJNMxEkFcwIo6VXhkHnIKw1pykm2Gs2bpjJ5W78nEAQusVKO2rkVoNYIvBIvrkk6fNEPTWcRwXN2cR055Mac8J5G4GwIqQ5f9krpFU23Q30JAkHgEm4kUl1J52iuj9Acc7czWTnLDWoc+i0GpzN0t8SIRrw1/d0IKx5NZQkJdDXXO/adG8HppLLhVIbtzfWeyaXQOLU4qOhKEJF2EXlURM6af9tWOeaoiPxMRJ4XkWdF5DdK9v2NiFwQkRPm42gl4/ETblz0IylvNKQpx+nfYmg66zn/AJQklTnoOB9J59jeHCMS9pZQ3N6s80ucMw15zVEMJb2Lbf4dKv3v3w88rpTaDzxuvi4nA/y2Uup64A7gMyKSKNn/75RSR83HiQrH4xuM7F4cTaQa9VjikCZWF6ajqd6x7OKhqYzn/AMAXS3GnHDWNJT1nKkQjBj6pIO5BEMeaUhTzrbGKNFIyPbIoUoFwV3AA+bzB4B3lx+glHpJKXXWfD4CXAU6K/xe31MXDtHVHHMka1BjCALvXfRglqN24AaYLxS5MpPzREOacqKREB1N9Y5qBKMp7zQpKqfPoQY1xaJiNJXzpEYgImavCm8Lgi6l1Kj5/ArQda2DReQWIAqcL9n8CdNk9GkRWdNQKSL3ichxETk+Pj5e4bC9QU8i5tjqb6GwxIQHk8k0vYmYIxrBaDpLUXkvYkija9A7gVLK6F/t0TnRl3CmQc34nNGbwmvJZJruFvvDitcVBCLymIicWuVxV+lxyuiesGawq4j0AH8L/K9KKR0w/hHgIPBqoB34o7Xer5T6vFLqmFLqWGdndSgUTiZSjaUXAO9FRWh6E0aZCbubcOgbixd9BGDMCacKz6Uyi+QWi57WEsdnF2zPJfBqDoHGifvEuoJAKXW7UuqGVR7fAsbMG7y+0V9d7TNEpAX4LvDHSqknSj57VBksAH8N3GLFSfkF3a/XiQ5EyzXnPbr6SybiLBSKTM7bGy89OOXNHAKNUWbC4Tnh0ZWw/h/ZHUTgtYY05fS0xhizuZFVpaahh4B7zef3At8qP0BEosA3gS8rpb5Rtk8LEcHwL5yqcDy+ItkaY6FQZMrmmx+sZJB60TEIzjUjGZrOEg6JZ1fByUSM+fwSMzn7u3OtNCny5m/hVAip1xrSlNOTiFMoKibmFmz7jkoFwSeBt4jIWeB28zUickxEvmAe8+vAvwB+Z5Uw0b8TkeeA54AO4D9WOB5f0bOcS2C/KcBrXajK6XVo9Tc4naGn1Xvhkhptr3cilHbUJxqB7YIglSHRUEdjvTeaNZWz3JfARpNhRWeulJoE3rzK9uPA+83n/w34b2u8/7ZKvt/v6BXI4HSGI32ttn7XaCpHa7yOhqg3J7tTLSuHprOeNQtBSdx4OsuhnhZbv2sknSMSEjqavJVMpulqiREJie2RQ8MeDR3VrGQXZzm6I7HO0VvDm8uiGmFvZxMi8NLYrO3fNZr2Vs35clrjdTRGw7YLgsGpjGcdxVCSSeqAljiaytLV4p2OXOWEQ0JPIubI4sDLgmAlu9i+OREIAheJR8Ps3tbIi6NOCALv5hCAES+dTMRt9RHkFpe4Orvg2egQgM5msyuVAyGkI2nv5hBo7A4hVUp5NqtYk2ioI1YXsjXnKBAELnOwu5kzjmgEOc8VFiunt83eTmXa7u61hjSlhHVXKic0Ag/nEGjsTipLZRbJ5Jc8rRGICEmb+xIEgsBlDnQ3c3Fy3tbSw7nFJabm854rNVyODqe1i0GPx4trkg4kGhaLiivpnGfzSjS9bXHGZhZYKNhzfeiFh9fnhN29KgJB4DIHu5tRyl4/wUroqHdXPbDSoyGTtyd0Uq8svawRgDNdqSbmF1hcUp7NK9HoG7RdGtJKMpm3f4fulkAjqGoOdBuRIWeu2CcIVpLJvL36szuBaHAqS11Y2N7s7d+hJ2GYhuzsSuX1HAKN3SGkXm1IU04yYSSVFZbs6eIXCAKX2dneQLwuzIs2CoLli97zk93ei35oOkNvIu7ZKBmN7kplZ5a113MINHY3qBmeztIQDZNo8FaPjnJ6WuMUFVydtSepLBAELhMOCdd1NXFmbMa279AXvddXf3bnEgxOZz1vCwZn+jOM+EQj6DbDW+3TCIzFgVHcwLuU5pfYQSAIPMCB7mZbQ0hH0znaGuqI1Xmr+1I5Oqbdrhvg8HTG8/4BgP1dzYC95sIrMznqIyHaG6O2fYcVRMIhulvsyyUYmvZ26KhG+3Ls8hMEgsADHOxuYXI+z7hNap9XG9KUEw6JcdHbsPrL5AtMzOV9oRHsam+guT7Cc8Np275jJGUkGHp9JQz2hpAOp7ydTKZZ6VQWCIKq5WC3vSvAkVTW84lDmt42e0ruDvskOgSM7lzX97bwrI2CwC+LAzAih+wwDc0vFEhlFn2hETTXR2iMhm0LKw4EgQc4YAqCF6/Y4ye4MpPzbNXRcnRfAqsZnNblp72vEQAc6W3l9OgMizZFiWiNwA/0tsW5MpMjX7D2t/BLDgGYncoS8UAjqGa2NdXT2VxvS+RQNr9EKrPom9Vfb8K46K0Ok1tpSOOP3+GG3lbyhSJnx+Ys/+yp+Tyj6dyyL8Lr9LXFUWolH8YqvF5+uhyjZWWgEVQ1B7ubbTENeb38dDm9bXGWiooxi/0lQ9NZ6iMhOpu9WWmznCO9RjXaUzaYh04OpQBsq2RpNXaFkA6l/GMuBGwtMxEIAo9woKuZl8ZmLe9CtJI45JPJblODmsGpDL1t3g8T1Oze1kiTTQ7jE5dTiGB76XOr2GFTg5rh6SzRcIhOj5bhLqcnETP6K1tsIoNAEHiGgz0tLBSKXJyct/Rz/ZJDoOlN2JNdPDSd9XT56XJCIeH6ZIs9gmAwxXXbm2nyaCOWcrpbY4TEeo1gOJWlJxEj5PEEQ01PawylYGzGeq2gIkEgIu0i8qiInDX/tq1x3FJJd7KHSrb3i8jPReSciHzVbGtZk9gVOTTq8RaV5WgTltUO48HpjG9MABrtMLbSX6KU4uRQyjdmIYA6M5dgyPLFQcY3/gGAI70J3v/6fqIR69fvlX7i/cDjSqn9wOPm69XIKqWOmo87S7Z/Cvi0UmofMA28r8Lx+JZ925sICbw4am3k0Gg6S0dTlPqIt5PJNA3RCO2NUUsFwWxukVRmkR3t/tEIwDDdLBSKnL1qncP40mSGVGaRm3wkCMCeEFKvdyYr53CyhT9552G6Wqxf1FUqCO4CHjCfP4DRgH5DmA3rbwN0Q/tNvb/aiNWF2d3RaHnk0GjaP6Gjml6LG9T4pcJkOTeYDmMrzUN+cxRr+tqsnRMLBaNJkR9yCJygUkHQpZQaNZ9fAbrWOC4mIsdF5AkR0Tf7bUBKKaVrDg8BvWt9kYjcZ37G8fHx8QqH7U0OdbdY3qRmcCrjG0exJmlxe8KV0FF/aQT9psPYysihZy6niNeFua6rybLPdILetjij6axleRU6iMIPOQROsK4gEJHHROTUKo+7So9TSilgrZCXXUqpY8BvAp8Rkb2bHahS6vNKqWNKqWOdnZ2bfbsvONDdzOWpDPML1tTjn5rPc3583nerv95EAyOpLMaUqpzBKZ1M5i+BGAoJhy12GJ8YTHGkt5VI2F9xIn1tRvVNq3IJ/FJ+2inWnQ1KqduVUjes8vgWMCYiPQDm36trfMaw+XcA+CFwMzAJJEREhy70AcMVn5GPOWBxk5onL0wC8Jo97ZZ8nlMkEzEyZiKcFQyZpYa9XmBtNax0GOcLRV4YmeHoTn8tDGBl5W6Vn8BPJUecoNJlwUPAvebze4FvlR8gIm0iUm8+7wBeB7xgahA/AN5zrffXEocsblLzxMAU8bowR3r9deHri9Mq85COGPJLDkEpR3pbyS0WOTdeucP49OgM+aUiN/X5az6A9UllQ6ksIfFPNJ3dVCoIPgm8RUTOArebrxGRYyLyBfOYQ8BxETmJceP/pFLqBXPfHwEfFpFzGD6DL1Y4Hl/T1xanIWpdk5onBiY5trvNlnAzO0la3JfAbzkEpSw7jIcqNw+dGDQdxT7UCHpa44hYqxF0tcSo85mJzC4qyihRSk0Cb15l+3Hg/ebznwJH1nj/AHBLJWOoJkIh4bquZkuKz03N53nxyizvuilpwcicpdfC7GKlFENTGW7ZvWqKi+fZ09FIYzTMqeE0v3ZsR0WfdXIwRWdzvedblq5GNBKiq9m6IAK/5RDYTSAOPcahHqPmUKWOUr/6BwDaG6PE6kKWZBfPZAvMLhR8Gx1iZBi3WuIwPjGY4qa+hC9NZGBtX4LhlD8a0jhFIAg8xoGuZqYzixU3qfGrfwCMkrtJi8pR6/LTfuhMthY39LbyQoUO43RmkYGJeW72oVlIYwiCyufEUlFxJZ0LNIISAkHgMQ6YDuPTFfoJ/Oof0PQm4pZoBEM+60OwGkf6WsgtFjk/vvU6VDqRzI+OYo2RS1B5ifKxmRyFovL1nLAaf94lqpiVmkNb9xNo/8Br9myzaliOY1WDGr8mk5VyxIIM4xODRsXRG3f4o+LoavS1NVhSonw5hyAwDS0TCAKP0dYYpaulsiY1fvYPaHoTcSbm8uQWlyr6nMGpDM31EVri/qi0uRr9HU00mA7jrXJyMMXeziZaYnUWjsxZlkNIpyrzE/itIY0TBILAgxzobuHF0a0LAj/7BzR6tVapeWhoOktfe4NvHaQA4QpLUiullh3FfsaqpLIgq/iVBILAgxzsbubc+NyWbaF+9w+AdbkEfiw/vRo39LbywsjMlhoXDU1nmZzP+zJ/oBTdU6NSQTA0nWVbY5R41B8VeZ3Av3eKKuZgdzP5LTapqQb/AFjToEYp5etkslKO9LaSXVzi/BYyjJcTyXyuEcTqwmxvrmc4VZlpaGg6E/gHyggEgQc5YDqMT2/BPFQN/gFY6UpVSVLZ1HyeTH6pKjSCIxVkGJ8YTFEfCXGwxx/N6q+FFSGkwyl/9SFwgkAQeJB925sIh2RLNYeqwT8ARleqrpYYw6mtV5tcjhjyWUOa1djTaTiMt+InODmY4obe1qoop1BpgxqlFCOBIHgF/p8ZVUh9JMyeLTapqQb/gMYIId26GWBw2p/lp1cjHBIO97RsOnJocanIc8Np3zuKNb1tRn7JVnwlAJPzeXKLxaqYE1bi/7tFlXKgu5kzY5vLJagW/4Cm0uxiv3YmW4sbelt5fpMO4zNXZlkoFH3vKNb0tcUpFBVXZ7emKS6HjlaB38hKAkHgUQ52NzM4lWVuE01qfj5QHf4BTW9bnCvp3JZXf4NTGRINdTT7OHa+FO0wHtiEw1g7im/2WXOitag0hDQIHV2dQBB4lINb6E3wxMBkVfgHNMlEnMUlteW6S9USMaQ50rf5DOMTgynaG6NVoxVV2pdgRSOojt/DKgJB4FEOLJea2IwgmKoa/wBAX4W5BENVkkOg2dvZRLxucw7jk4Mpju7wb8XRcvRKfmhq6xpBc32E1nh1aIlWUR13jCqkry1OU31kw70JJucWODNWPf4BWFm1bUUQ6ByCahIEYbOH8UYdxrO5Rc6Nz1WNoxiMXIKOpvqKFgeBNvBKKhIEItIuIo+KyFnz7yu6f4jIm0TkRMkjJyLvNvf9jYhcKNl3tJLxVBMiwoHu5g1HDj15YQqgqgRBsoIGNeNzCywUilUROlrKkU04jJ8bSqOUPzuSXYtKcgmGpoPQ0dWoVCO4H3hcKbUfeNx8/TKUUj9QSh1VSh0FbgMywP8oOeTf6f1KqRMVjqeqONC98SY12j9wY59/q0uW02Sq8FvJLh6cqq6IIc0Nva1k8ktcmFjfYfzMoC49XT1zAiprUBM0pFmdSgXBXcAD5vMHgHevc/x7gO8ppaxpM1TlHOxuJp1d5MrM+qFy2j9QDUlDpWw1hFTfKKrJWQybK0l9YjBFf0cjiYao3cNyFCOXIEdxk9FkM7lFZnOFqlscWEGld40updSo+fwK0LXO8XcDXynb9gkReVZEPi0i9Wu9UUTuE5HjInJ8fHy8giH7Bx05tJ55qBr9A5qtNqgZqtLokL2djcTqQjy7TqkJXXH0aJWEjZbS19ZAfqnI+NzmoslWyk9X1+LACtYVBCLymIicWuVxV+lxyrBfrCmiRaQHo4n9IyWbPwIcBF4NtAN/tNb7lVKfV0odU0od6+zsXG/YVcGBro1FDlWjf0DT1xbfko9gaDpDR1OUhqh/+xCsRiQc2lCG8Wg6x/jsQpUKgq2FkAaho2uz7lWilLp9rX0iMiYiPUqpUfNGf/UaH/XrwDeVUosln621iQUR+WvgDzc47pqgtaGOntYYL45eO3KoGv0DmmQixuxCgXR2cVMhf4NT2arNHj3S28rXnx5iqagIh1YPCz2p/QNVKAh2LAuCLL+wa+PvC5LJ1qZS09BDwL3m83uBb13j2HsoMwuZwgMxgpzfDZyqcDxVx0Yih6rVPwAravxmzUND05nlG0a1sRGH8YnBFNFwiENVUHG0HB1NttnIoeFUlvpIiI6m6vKZWEGld45PAm8RkbPA7eZrROSYiHxBHyQiu4EdwI/K3v93IvIc8BzQAfzHCsdTdRzsbuH8+ByLazSpqWb/ABgaAcDTl6Y3/J6lomI4la3a5uQbyTB+ZjDFoWQL9ZHqa77SEI2wrTG6adPQ0HSG3kS8apLrrKQiQaCUmlRKvVkptV8pdbtSasrcflwp9f6S4y4qpXqVUsWy99+mlDqilLpBKfVbSqnNd92ocg52N7O4pLgwsXqTmmr2D4AhCPd2NvIn/3CK3/u7pzcUQXR1NsfikmJHe3VqBPs6m4jVhXhuaHWTYWGpyHND6aqpL7QaW8klGJ4OQkfXovpsCVXGSpOa1S/6avYPAMSjYb77oTfw4bdcx/dfvMqb//SH/PnjZ6/Z1H4lh6A6NYJIOMShaziMz16dI7u4VJWOYk1fW8OmgwiChjRrU10hFVXI3s4mItdoUlPN/gFNrC7Mh968n195VS//z8On+dNHX+LrTw/x0Xce5s2Hg4l6rgAACYtJREFUtr9C1V/JIajei/5Ibyv//ekhikVFqMxhfKKKHcWa3rY4j54eW/X8AcZnFzg1kubUUNr4OzzDxFy+6jLNrSIQBB4nGgmxt7NpVUGg/QN3Hk26MDLn6Wtr4L++9xf4p3MTfOyh53n/l4/zxgOdfOxd19Pf0bh8nDYZJKt49XdDbytf/tklBibm2be96WX7Tg6mSDTUsXtb9d70+tri5AtFJuYWUMCp4TTPDac5NWzc9EuTMPs7GnnVrjbu/V928RvHdro3aA8TCAIfcKC7eVVnabX7B9bidfs6+N4fvIEHfnqRzzx2lrd9+se87w39fPBN+2isjzA4laGrpZ5YXfU5SjU6w/jUcPoVguDEYIqb+qqn4uhq6FyC2//sR8zkjJ4dIrCno5HX7Gnnht5Wbuht5fpkS9X0o7CTQBD4gIM9zTx0coSZ3CItJZO62v0D16IuHOL9b9jDnUeTfPJ7L/IXPzzPN/95mP/rlw4xOJ2pWv+AZv/2JuojIZ4bTvPum3uXt88vFHhpbJa3Xt/t4ujs51U723jD/g46m+u5IdnKkb5WDve00Fgf3NK2QvCr+YCDpsP4pSuzHNu90n2sFvwD67G9Ocaf/fpR3nvrTj720PN86CvPAHBXlZvLtMO4PIT0ueE0RVU9HcnWItEQ5W/fd6vbw6gaavcO4iMOmDWHTpf4Cao9f2Cz/MKudr71gdfziV++ga6Wem7pr452ndfiSG8rL4zMvKz4mnYU16KWGLB1Ao3AByRbYzTHIpwpaVJTq/6BaxEOCe+9dRfvvXUTdQd8zJHeVv72iUtcmJxnb6fhJzg5mGJnewPbmtas3xgQ8AoCjcAHiAgHzd4Emlr2DwQY3FDiMNZUa8XRAHsJBIFP0DWHdJOawD8QsL+riWgkxHNmSeqxmRyj6VxV5w8E2ENwF/EJB7pbmM0VGEnnAv9AAGBETpU6jLV/INAIAjZL4CPwCYe6dW+CGRYWjZJNgSAIONLbwj88M0KxaDSiiYSE65Mtbg8rwGcEGoFPuM4UBC9emeWJgUkaooF/IMBwGM8tFLg4Oc/JwRSHelqqOpEuwB4CQeATWmJ19CbivDg6a/oH2gP/QMCyw/jZoTTPDqUDs1DAlgjuJD7iYHczT16YMv0D1R8nH7A+13U1E42E+IcTw8wtFAJHccCWCASBjzjQ3bxcTCvwDwSA6TDubuZHL40DgaM4YGtUJAhE5NdE5HkRKYrIsWscd4eInBGRcyJyf8n2fhH5ubn9qyIS9JC7Bro3QUM0vFx0LCDght5WlILmWIQ9JVVYAwI2SqUawSngV4Afr3WAiISBzwJvBw4D94jIYXP3p4BPK6X2AdPA+yocT1VzqMeIBgn8AwGl6EXBTX2JVWvzBwSsR6WtKk8rpc6sc9gtwDml1IBSKg88CNxlNqy/DfiGedwDGA3sA9agv6ORrpZ63nZ9l9tDCfAQ2mEcmIUCtooTeQS9wGDJ6yHgVmAbkFJKFUq297IGInIfcB/Azp212VyiLhziiY+82e1hBHiMQz0tfOBNe/n1YzvcHkqAT1lXEIjIY8Bqxc3/WCn1LeuHtDpKqc8Dnwc4duyYWufwqqWam40EbI1wSPh3bzvo9jACfMy6gkApdXuF3zEMlC5V+sxtk0BCRCKmVqC3BwQEBAQ4iBMex6eA/WaEUBS4G3hIGdXTfgC8xzzuXsAxDSMgICAgwKDS8NFfFpEh4LXAd0XkEXN7UkQeBjBX+x8EHgFOA19TSj1vfsQfAR8WkXMYPoMvVjKegICAgIDNI7qssZ84duyYOn78uNvDCAgICPAVIvK0UuoVOV9BMHpAQEBAjRMIgoCAgIAaJxAEAQEBATVOIAgCAgICahxfOotFZBy4tMW3dwATFg7HDwTnXBsE51z9VHq+u5RSneUbfSkIKkFEjq/mNa9mgnOuDYJzrn7sOt/ANBQQEBBQ4wSCICAgIKDGqUVB8Hm3B+ACwTnXBsE5Vz+2nG/N+QgCAgICAl5OLWoEAQEBAQElBIIgICAgoMapKUEgIneIyBkROSci97s9HqsQkS+JyFUROVWyrV1EHhWRs+bfNnO7iMh/MX+DZ0XkVe6NfGuIyA4R+YGIvCAiz4vIH5jbq/mcYyLypIicNM/5/za394vIz81z+6pZ6h0RqTdfnzP373Zz/JUgImEReUZEvmO+rupzFpGLIvKciJwQkePmNlvnds0IAhEJA58F3g4cBu4RkcPujsoy/ga4o2zb/cDjSqn9wOPmazDOf7/5uA/4C4fGaCUF4N8qpQ4DrwE+YP4vq/mcF4DblFI3AUeBO0TkNcCngE8rpfYB08D7zOPfB0yb2z9tHudX/gCjhL2mFs75TUqpoyU5A/bObaVUTTwweiY8UvL6I8BH3B6Xhee3GzhV8voM0GM+7wHOmM8/B9yz2nF+fWA0NHpLrZwz0AD8M0bv7wkgYm5fnuMY/T9eaz6PmMeJ22Pfwrn2mTe+24DvAFID53wR6CjbZuvcrhmNAOgFBkteD5nbqpUupdSo+fwK0GU+r6rfwVT/bwZ+TpWfs2kiOQFcBR4FzgMpZTR/gpef1/I5m/vTGM2f/MZngP8TKJqvt1H956yA/yEiT4vIfeY2W+f2uj2LA/yPUkqJSNXFCYtIE/Dfgf9DKTUjIsv7qvGclVJLwFERSQDfBKq6Y72IvBO4qpR6WkTe6PZ4HOT1SqlhEdkOPCoiL5butGNu15JGMAzsKHndZ26rVsZEpAfA/HvV3F4Vv4OI1GEIgb9TSv29ubmqz1mjlEph9Pt+LZAQEb2gKz2v5XM297cCkw4PtVJeB9wpIheBBzHMQ/8v1X3OKKWGzb9XMQT+Ldg8t2tJEDwF7DcjDqLA3cBDLo/JTh4C7jWf34thR9fbf9uMNngNkC5ROX2BGEv/LwKnlVJ/VrKrms+509QEEJE4hk/kNIZAeI95WPk569/iPcD3lWlE9gtKqY8opfqUUrsxrtfvK6XeSxWfs4g0ikizfg68FTiF3XPbbceIw06YdwAvYdhW/9jt8Vh4Xl8BRoFFDBvh+zBso48DZ4HHgHbzWMGInjoPPAccc3v8Wzjf12PYUZ8FTpiPd1T5Od8IPGOe8yngo+b2PcCTwDng60C9uT1mvj5n7t/j9jlUeP5vBL5T7edsnttJ8/G8vk/ZPbeDEhMBAQEBNU4tmYYCAgICAlYhEAQBAQEBNU4gCAICAgJqnEAQBAQEBNQ4gSAICAgIqHECQRAQEBBQ4wSCICAgIKDG+f8BSdvC14BdqU0AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5GIhDlyzL6ui"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mS7_n852MEBq"
      },
      "source": [
        "## 2. Pandas "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        },
        "id": "2yrDhjQvMF7G",
        "outputId": "d6df706a-4828-4f31-9543-45b67358bf28"
      },
      "source": [
        "#Step 1: Import Pandas with an alias 'pd'\n",
        "import pandas as pd\n",
        "\n",
        "\n",
        "#Step 2: Create your dictionary\n",
        "cambay_rocks = {'phi': [0.2,0.40,0.30,0.25,0.270],\n",
        "                'perm': [100,20,150,130,145],\n",
        "                'lith': ['sst','shale','sst','sst','sst']}\n",
        "\n",
        "#Step 3: Create your Table.\n",
        "rock_table = pd.DataFrame(cambay_rocks)\n",
        "\n",
        "#Step 4: Print your table.\n",
        "rock_table"
      ],
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>phi</th>\n",
              "      <th>perm</th>\n",
              "      <th>lith</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.20</td>\n",
              "      <td>100</td>\n",
              "      <td>sst</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.40</td>\n",
              "      <td>20</td>\n",
              "      <td>shale</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.30</td>\n",
              "      <td>150</td>\n",
              "      <td>sst</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.25</td>\n",
              "      <td>130</td>\n",
              "      <td>sst</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.27</td>\n",
              "      <td>145</td>\n",
              "      <td>sst</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    phi  perm   lith\n",
              "0  0.20   100    sst\n",
              "1  0.40    20  shale\n",
              "2  0.30   150    sst\n",
              "3  0.25   130    sst\n",
              "4  0.27   145    sst"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 215
        },
        "id": "W8Gtm3IVMPBc",
        "outputId": "08a3b9c3-2a5d-4529-fefa-e6a59e667793"
      },
      "source": [
        "df = pd.read_csv('/content/sample_data/california_housing_train.csv')\n",
        "\n",
        "\n",
        "#Similarly excel file can be read by- \n",
        "#df = pd.read_excel('\\path\\filename.csv')\n",
        "\n",
        "df.head()"
      ],
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>longitude</th>\n",
              "      <th>latitude</th>\n",
              "      <th>housing_median_age</th>\n",
              "      <th>total_rooms</th>\n",
              "      <th>total_bedrooms</th>\n",
              "      <th>population</th>\n",
              "      <th>households</th>\n",
              "      <th>median_income</th>\n",
              "      <th>median_house_value</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>-114.31</td>\n",
              "      <td>34.19</td>\n",
              "      <td>15.0</td>\n",
              "      <td>5612.0</td>\n",
              "      <td>1283.0</td>\n",
              "      <td>1015.0</td>\n",
              "      <td>472.0</td>\n",
              "      <td>1.4936</td>\n",
              "      <td>66900.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-114.47</td>\n",
              "      <td>34.40</td>\n",
              "      <td>19.0</td>\n",
              "      <td>7650.0</td>\n",
              "      <td>1901.0</td>\n",
              "      <td>1129.0</td>\n",
              "      <td>463.0</td>\n",
              "      <td>1.8200</td>\n",
              "      <td>80100.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-114.56</td>\n",
              "      <td>33.69</td>\n",
              "      <td>17.0</td>\n",
              "      <td>720.0</td>\n",
              "      <td>174.0</td>\n",
              "      <td>333.0</td>\n",
              "      <td>117.0</td>\n",
              "      <td>1.6509</td>\n",
              "      <td>85700.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-114.57</td>\n",
              "      <td>33.64</td>\n",
              "      <td>14.0</td>\n",
              "      <td>1501.0</td>\n",
              "      <td>337.0</td>\n",
              "      <td>515.0</td>\n",
              "      <td>226.0</td>\n",
              "      <td>3.1917</td>\n",
              "      <td>73400.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-114.57</td>\n",
              "      <td>33.57</td>\n",
              "      <td>20.0</td>\n",
              "      <td>1454.0</td>\n",
              "      <td>326.0</td>\n",
              "      <td>624.0</td>\n",
              "      <td>262.0</td>\n",
              "      <td>1.9250</td>\n",
              "      <td>65500.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   longitude  latitude  ...  median_income  median_house_value\n",
              "0    -114.31     34.19  ...         1.4936             66900.0\n",
              "1    -114.47     34.40  ...         1.8200             80100.0\n",
              "2    -114.56     33.69  ...         1.6509             85700.0\n",
              "3    -114.57     33.64  ...         3.1917             73400.0\n",
              "4    -114.57     33.57  ...         1.9250             65500.0\n",
              "\n",
              "[5 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zomysjr2MZkF"
      },
      "source": [
        "Description-\n",
        "\n",
        "Note that the Pandas DF has two parts-\n",
        "\n",
        "The vertical Columns.\n",
        "The Horizontal Rows."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "m2cUDHRzMXLs",
        "outputId": "22b6cbb9-5bda-4ecf-bd69-b72a664595d3"
      },
      "source": [
        "df.shape"
      ],
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(17000, 9)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_egNS_rmM1re"
      },
      "source": [
        "Descriptive Statistics of each feature."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 304
        },
        "id": "x8QfHr0dMtxA",
        "outputId": "20509765-4711-4057-cdc7-3a296ba4a5be"
      },
      "source": [
        "df.describe()"
      ],
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>longitude</th>\n",
              "      <th>latitude</th>\n",
              "      <th>housing_median_age</th>\n",
              "      <th>total_rooms</th>\n",
              "      <th>total_bedrooms</th>\n",
              "      <th>population</th>\n",
              "      <th>households</th>\n",
              "      <th>median_income</th>\n",
              "      <th>median_house_value</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>17000.000000</td>\n",
              "      <td>17000.000000</td>\n",
              "      <td>17000.000000</td>\n",
              "      <td>17000.000000</td>\n",
              "      <td>17000.000000</td>\n",
              "      <td>17000.000000</td>\n",
              "      <td>17000.000000</td>\n",
              "      <td>17000.000000</td>\n",
              "      <td>17000.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>-119.562108</td>\n",
              "      <td>35.625225</td>\n",
              "      <td>28.589353</td>\n",
              "      <td>2643.664412</td>\n",
              "      <td>539.410824</td>\n",
              "      <td>1429.573941</td>\n",
              "      <td>501.221941</td>\n",
              "      <td>3.883578</td>\n",
              "      <td>207300.912353</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>2.005166</td>\n",
              "      <td>2.137340</td>\n",
              "      <td>12.586937</td>\n",
              "      <td>2179.947071</td>\n",
              "      <td>421.499452</td>\n",
              "      <td>1147.852959</td>\n",
              "      <td>384.520841</td>\n",
              "      <td>1.908157</td>\n",
              "      <td>115983.764387</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>-124.350000</td>\n",
              "      <td>32.540000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.499900</td>\n",
              "      <td>14999.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>-121.790000</td>\n",
              "      <td>33.930000</td>\n",
              "      <td>18.000000</td>\n",
              "      <td>1462.000000</td>\n",
              "      <td>297.000000</td>\n",
              "      <td>790.000000</td>\n",
              "      <td>282.000000</td>\n",
              "      <td>2.566375</td>\n",
              "      <td>119400.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>-118.490000</td>\n",
              "      <td>34.250000</td>\n",
              "      <td>29.000000</td>\n",
              "      <td>2127.000000</td>\n",
              "      <td>434.000000</td>\n",
              "      <td>1167.000000</td>\n",
              "      <td>409.000000</td>\n",
              "      <td>3.544600</td>\n",
              "      <td>180400.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>-118.000000</td>\n",
              "      <td>37.720000</td>\n",
              "      <td>37.000000</td>\n",
              "      <td>3151.250000</td>\n",
              "      <td>648.250000</td>\n",
              "      <td>1721.000000</td>\n",
              "      <td>605.250000</td>\n",
              "      <td>4.767000</td>\n",
              "      <td>265000.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>-114.310000</td>\n",
              "      <td>41.950000</td>\n",
              "      <td>52.000000</td>\n",
              "      <td>37937.000000</td>\n",
              "      <td>6445.000000</td>\n",
              "      <td>35682.000000</td>\n",
              "      <td>6082.000000</td>\n",
              "      <td>15.000100</td>\n",
              "      <td>500001.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          longitude      latitude  ...  median_income  median_house_value\n",
              "count  17000.000000  17000.000000  ...   17000.000000        17000.000000\n",
              "mean    -119.562108     35.625225  ...       3.883578       207300.912353\n",
              "std        2.005166      2.137340  ...       1.908157       115983.764387\n",
              "min     -124.350000     32.540000  ...       0.499900        14999.000000\n",
              "25%     -121.790000     33.930000  ...       2.566375       119400.000000\n",
              "50%     -118.490000     34.250000  ...       3.544600       180400.000000\n",
              "75%     -118.000000     37.720000  ...       4.767000       265000.000000\n",
              "max     -114.310000     41.950000  ...      15.000100       500001.000000\n",
              "\n",
              "[8 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KIoZRkBgNASp"
      },
      "source": [
        "### Accessing Columns"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-Y6D0E25M5vq",
        "outputId": "1b67b9a1-26b7-4b61-e57d-1105208f6a40"
      },
      "source": [
        "df.columns"
      ],
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['longitude', 'latitude', 'housing_median_age', 'total_rooms',\n",
              "       'total_bedrooms', 'population', 'households', 'median_income',\n",
              "       'median_house_value'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3EPOoL7RNQHK",
        "outputId": "af606baa-e1d3-49ec-ddba-06e682f453e6"
      },
      "source": [
        "rock_table.columns"
      ],
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['phi', 'perm', 'lith'], dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        },
        "id": "LLOW0O_oM9En",
        "outputId": "9ff29573-c6d0-466c-e34b-b0e4e97e42cf"
      },
      "source": [
        "rock_table[['phi','perm']]"
      ],
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>phi</th>\n",
              "      <th>perm</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.20</td>\n",
              "      <td>100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.40</td>\n",
              "      <td>20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.30</td>\n",
              "      <td>150</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.25</td>\n",
              "      <td>130</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.27</td>\n",
              "      <td>145</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    phi  perm\n",
              "0  0.20   100\n",
              "1  0.40    20\n",
              "2  0.30   150\n",
              "3  0.25   130\n",
              "4  0.27   145"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gt978toaNgWe"
      },
      "source": [
        "### Accessing Rows."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 215
        },
        "id": "bWoU0aPDNQsl",
        "outputId": "ea55a86a-bdd0-4c76-b786-94479c358db5"
      },
      "source": [
        "#Accessing 5th to 9th index rows, and all columns.\n",
        "df.iloc[5:10, :]"
      ],
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>longitude</th>\n",
              "      <th>latitude</th>\n",
              "      <th>housing_median_age</th>\n",
              "      <th>total_rooms</th>\n",
              "      <th>total_bedrooms</th>\n",
              "      <th>population</th>\n",
              "      <th>households</th>\n",
              "      <th>median_income</th>\n",
              "      <th>median_house_value</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>-114.58</td>\n",
              "      <td>33.63</td>\n",
              "      <td>29.0</td>\n",
              "      <td>1387.0</td>\n",
              "      <td>236.0</td>\n",
              "      <td>671.0</td>\n",
              "      <td>239.0</td>\n",
              "      <td>3.3438</td>\n",
              "      <td>74000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>-114.58</td>\n",
              "      <td>33.61</td>\n",
              "      <td>25.0</td>\n",
              "      <td>2907.0</td>\n",
              "      <td>680.0</td>\n",
              "      <td>1841.0</td>\n",
              "      <td>633.0</td>\n",
              "      <td>2.6768</td>\n",
              "      <td>82400.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>-114.59</td>\n",
              "      <td>34.83</td>\n",
              "      <td>41.0</td>\n",
              "      <td>812.0</td>\n",
              "      <td>168.0</td>\n",
              "      <td>375.0</td>\n",
              "      <td>158.0</td>\n",
              "      <td>1.7083</td>\n",
              "      <td>48500.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>-114.59</td>\n",
              "      <td>33.61</td>\n",
              "      <td>34.0</td>\n",
              "      <td>4789.0</td>\n",
              "      <td>1175.0</td>\n",
              "      <td>3134.0</td>\n",
              "      <td>1056.0</td>\n",
              "      <td>2.1782</td>\n",
              "      <td>58400.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>-114.60</td>\n",
              "      <td>34.83</td>\n",
              "      <td>46.0</td>\n",
              "      <td>1497.0</td>\n",
              "      <td>309.0</td>\n",
              "      <td>787.0</td>\n",
              "      <td>271.0</td>\n",
              "      <td>2.1908</td>\n",
              "      <td>48100.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   longitude  latitude  ...  median_income  median_house_value\n",
              "5    -114.58     33.63  ...         3.3438             74000.0\n",
              "6    -114.58     33.61  ...         2.6768             82400.0\n",
              "7    -114.59     34.83  ...         1.7083             48500.0\n",
              "8    -114.59     33.61  ...         2.1782             58400.0\n",
              "9    -114.60     34.83  ...         2.1908             48100.0\n",
              "\n",
              "[5 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IUejM0N5OYTy"
      },
      "source": [
        "### loc and iloc\n",
        "\n",
        "\" l for label , i for index\""
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        },
        "id": "8RHLlx9kNjTx",
        "outputId": "c92218d7-3b52-40ba-9428-b410f8beabbd"
      },
      "source": [
        "df.iloc[0:5 , 0:3]"
      ],
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>longitude</th>\n",
              "      <th>latitude</th>\n",
              "      <th>housing_median_age</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>-114.31</td>\n",
              "      <td>34.19</td>\n",
              "      <td>15.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-114.47</td>\n",
              "      <td>34.40</td>\n",
              "      <td>19.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-114.56</td>\n",
              "      <td>33.69</td>\n",
              "      <td>17.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-114.57</td>\n",
              "      <td>33.64</td>\n",
              "      <td>14.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-114.57</td>\n",
              "      <td>33.57</td>\n",
              "      <td>20.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   longitude  latitude  housing_median_age\n",
              "0    -114.31     34.19                15.0\n",
              "1    -114.47     34.40                19.0\n",
              "2    -114.56     33.69                17.0\n",
              "3    -114.57     33.64                14.0\n",
              "4    -114.57     33.57                20.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        },
        "id": "oqOOBDrdOcbO",
        "outputId": "be7da7a3-bf92-4104-c2ef-9ef954fdddcf"
      },
      "source": [
        "df.loc[0:4, 'longitude':'housing_median_age']"
      ],
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>longitude</th>\n",
              "      <th>latitude</th>\n",
              "      <th>housing_median_age</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>-114.31</td>\n",
              "      <td>34.19</td>\n",
              "      <td>15.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-114.47</td>\n",
              "      <td>34.40</td>\n",
              "      <td>19.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-114.56</td>\n",
              "      <td>33.69</td>\n",
              "      <td>17.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-114.57</td>\n",
              "      <td>33.64</td>\n",
              "      <td>14.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-114.57</td>\n",
              "      <td>33.57</td>\n",
              "      <td>20.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   longitude  latitude  housing_median_age\n",
              "0    -114.31     34.19                15.0\n",
              "1    -114.47     34.40                19.0\n",
              "2    -114.56     33.69                17.0\n",
              "3    -114.57     33.64                14.0\n",
              "4    -114.57     33.57                20.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lY8JFnJ-OjZU"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NGrVRFVbPh5p"
      },
      "source": [
        "## 3. Plotting : F S P L L G\n",
        "\n",
        "1. F- Figure (figsize=(h,v))\n",
        "2. S- Style (default)\n",
        "3. P - Plot(plt.plot(x,y,label='')\n",
        "4. L- Labels for axes (plt.xlabel)\n",
        "5. L - Legend (plt.legend)\n",
        "6. G - Grid (plt.grid())"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 462
        },
        "id": "hJy3c1KaP_ln",
        "outputId": "ee8ba136-b327-4b84-c534-18bd048f2fb9"
      },
      "source": [
        "#1. F : figsize\n",
        "plt.figure(figsize=(8,5))\n",
        "\n",
        "#2. S : Style\n",
        "plt.style.use('default')\n",
        "\n",
        "#3. P : Plot\n",
        "plt.plot(Sw,krw,label='Krw')\n",
        "plt.plot(Sw,kro,label='Kro')\n",
        "\n",
        "#4. L : Labels\n",
        "plt.xlabel('Sw') ; plt.ylabel('Krel')\n",
        "\n",
        "#5. Legend\n",
        "plt.legend()\n",
        "\n",
        "#6. Grid\n",
        "plt.grid()\n",
        "\n",
        "#7. title\n",
        "plt.title('Rel Perm')"
      ],
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Rel Perm')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 70
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gmEsP77HQ1Xj"
      },
      "source": [
        "## Data Analysis Excercise : Fractional Flow\n",
        "\n",
        ">fw = 1/(1 + 1/M) M = (krw/mu_w)/(kro/mu_o)\n",
        "\n",
        "Assume mu_o = 1000 cp (heavy oil) mu_w = 1 cp"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H9xPx9OQRN3E"
      },
      "source": [
        "import warnings\n",
        "warnings.filterwarnings('ignore')"
      ],
      "execution_count": 76,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "tira1YRkQdnc",
        "outputId": "300b724f-59e7-4e10-adc6-fc577d6b218c"
      },
      "source": [
        "sw = np.linspace(0.01,1,50)\n",
        "\n",
        "mu_o = 1000\n",
        "\n",
        "mu_w = 1\n",
        "\n",
        "M1 = (krw/mu_w)/(kro/mu_o)\n",
        "\n",
        "fw = 1/(1 + (1/M1))\n",
        "\n",
        "plt.plot(sw,fw,label = 'Thin water-Thick oil')\n",
        "\n",
        "M2 = (krw/100)/(kro/mu_o)\n",
        "\n",
        "fw2 = 1/(1+(1/M2))\n",
        "\n",
        "plt.plot(sw,fw2,label='Thick water-Thick oil')\n",
        "\n",
        "\n",
        "plt.xlabel('Sw or Recovery Factor') ; plt.ylabel('fw')\n",
        "plt.legend(loc='best')"
      ],
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f66fef142d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 79
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3lCXs89SQ9w2"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SGUNlokPSZBX"
      },
      "source": [
        "# 6. Machine Learning : Layman Introduction."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9ErjkOyvSecK"
      },
      "source": [
        "1. Supervised - Regression"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 225
        },
        "id": "KuwCf3FzSUHG",
        "outputId": "4c1e9e11-9b1a-4cbe-ed98-bd6a89a8f123"
      },
      "source": [
        "df_example = pd.DataFrame({'phi':[0.1,0.15,0.2,0.25,0.3,0.35],\n",
        " 'pore radius': [5,10,15,20,25,30],\n",
        " 'k': [80,100,120,150,180,'?']})\n",
        "\n",
        "\n",
        "df_example\n",
        "\n",
        "#this becomes a regression problem"
      ],
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>phi</th>\n",
              "      <th>pore radius</th>\n",
              "      <th>k</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.10</td>\n",
              "      <td>5</td>\n",
              "      <td>80</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.15</td>\n",
              "      <td>10</td>\n",
              "      <td>100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.20</td>\n",
              "      <td>15</td>\n",
              "      <td>120</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.25</td>\n",
              "      <td>20</td>\n",
              "      <td>150</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.30</td>\n",
              "      <td>25</td>\n",
              "      <td>180</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0.35</td>\n",
              "      <td>30</td>\n",
              "      <td>?</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    phi  pore radius    k\n",
              "0  0.10            5   80\n",
              "1  0.15           10  100\n",
              "2  0.20           15  120\n",
              "3  0.25           20  150\n",
              "4  0.30           25  180\n",
              "5  0.35           30    ?"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 82
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZXvb2OleS4R9"
      },
      "source": [
        "#### Resulting model : $ K = m_1 \\phi + m_2 r + c $"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ofLvJMOpTSVa"
      },
      "source": [
        "> 2. Supervised - Classification"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 284
        },
        "id": "_kpObkYdSXBY",
        "outputId": "a8ff69cb-ad32-407f-93c2-86a0fbba45bb"
      },
      "source": [
        "rock_labels = pd.DataFrame({'k_md':[100,150,200,5,90,6,80,4],\n",
        " 'phi': [0.25,0.27,0.22,0.44,0.26,0.38,0.21,0.34],\n",
        " 'RT(ohm-m)': [100,110,120,10,89,15,80,20],\n",
        " 'lith':['sst','sst','sst','shale','sst','shale','sst','???']})\n",
        "\n",
        "\n",
        "rock_labels\n",
        "#KNN | K-Means "
      ],
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>k_md</th>\n",
              "      <th>phi</th>\n",
              "      <th>RT(ohm-m)</th>\n",
              "      <th>lith</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>100</td>\n",
              "      <td>0.25</td>\n",
              "      <td>100</td>\n",
              "      <td>sst</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>150</td>\n",
              "      <td>0.27</td>\n",
              "      <td>110</td>\n",
              "      <td>sst</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>200</td>\n",
              "      <td>0.22</td>\n",
              "      <td>120</td>\n",
              "      <td>sst</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>5</td>\n",
              "      <td>0.44</td>\n",
              "      <td>10</td>\n",
              "      <td>shale</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>90</td>\n",
              "      <td>0.26</td>\n",
              "      <td>89</td>\n",
              "      <td>sst</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>6</td>\n",
              "      <td>0.38</td>\n",
              "      <td>15</td>\n",
              "      <td>shale</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>80</td>\n",
              "      <td>0.21</td>\n",
              "      <td>80</td>\n",
              "      <td>sst</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>4</td>\n",
              "      <td>0.34</td>\n",
              "      <td>20</td>\n",
              "      <td>???</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   k_md   phi  RT(ohm-m)   lith\n",
              "0   100  0.25        100    sst\n",
              "1   150  0.27        110    sst\n",
              "2   200  0.22        120    sst\n",
              "3     5  0.44         10  shale\n",
              "4    90  0.26         89    sst\n",
              "5     6  0.38         15  shale\n",
              "6    80  0.21         80    sst\n",
              "7     4  0.34         20    ???"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 84
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WIj0wR4LTlNX"
      },
      "source": [
        "### Steps in ML :-\n",
        "\n",
        "1. Import Data.\n",
        "2. Check for missing values and abnormal values.\n",
        "3. Accordingly process the data and make it usable.\n",
        "STEPS in a Machine Learning Project-4. Perform EDA - Exploratory Data Analysis. Check for which features are not important\n",
        "and which can be excluded.\n",
        "5. Record Visual stories of your data, to be presented.\n",
        "6. now pick a ML algrithm suitable to your case.\n",
        "7. Split the Data into Training, Validation and Test Data.\n",
        "8. Fit the ML-model into the training data.\n",
        "9. Perform predictions and validate, and make modifications based on the validation performance.\n",
        "10. Finally Use real, unseen data and make predictions (Production)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UXlGYVOaTI1D"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MVc5PHCmVxH1"
      },
      "source": [
        "# Hands on Excercise 1 : DCA with Python"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 225
        },
        "id": "pXaamQT4V1Xt",
        "outputId": "d219b1d5-6477-4d3b-8571-bf9ee6ce2975"
      },
      "source": [
        "dca_df = pd.read_csv('https://raw.githubusercontent.com/yohanesnuwara/pyreservoir/master/data/norne_production_rate_sample.csv',\n",
        "                     index_col = 0 , parse_dates = True)\n",
        "\n",
        "dca_df.head()"
      ],
      "execution_count": 87,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Rate (SCF/d)</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2004-04-01</th>\n",
              "      <td>2706039.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2004-05-01</th>\n",
              "      <td>2492086.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2004-06-02</th>\n",
              "      <td>1816846.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2004-07-02</th>\n",
              "      <td>1920207.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2004-07-04</th>\n",
              "      <td>1683521.4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            Rate (SCF/d)\n",
              "Date                    \n",
              "2004-04-01     2706039.0\n",
              "2004-05-01     2492086.2\n",
              "2004-06-02     1816846.1\n",
              "2004-07-02     1920207.4\n",
              "2004-07-04     1683521.4"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 87
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        },
        "id": "lwgrbGSiV_63",
        "outputId": "da317b37-ac59-46aa-e91d-033493455106"
      },
      "source": [
        "dca_df.plot(figsize=(12,4))\n",
        "plt.grid()"
      ],
      "execution_count": 92,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1200x400 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 383
        },
        "id": "-zPwhW9DWDju",
        "outputId": "e19e557d-160f-4546-b46a-ce124d09cf14"
      },
      "source": [
        "plt.figure(figsize=(12,4))\n",
        "plt.scatter(y=dca_df['Rate (SCF/d)'],x=dca_df.index,marker='*')\n",
        "\n",
        "# plt.ylabel('Rate (SCF/d)')\n",
        "plt.grid()"
      ],
      "execution_count": 94,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1200x400 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4WvosL5OXAzu"
      },
      "source": [
        "### Step 1 : Convert Dates into Days (t)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pY9C0vfUW5zg"
      },
      "source": [
        "def day_maker(df):\n",
        "    \n",
        "    '''\n",
        "    Pass a Time-Series DataFrame to it and it will\n",
        "    return a days column. Subtracts dates and makes days. \n",
        "    \n",
        "    Returned is a days (np array).\n",
        "    '''\n",
        "    \n",
        "    days = []\n",
        "\n",
        "    for d in range(len(df)):\n",
        "    \n",
        "        delta = df.index[d] - df.index[0]\n",
        "    \n",
        "        days.append(delta.days)\n",
        "    \n",
        "    days = np.array(days)\n",
        "    \n",
        "    return days"
      ],
      "execution_count": 95,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PASJJquqXOgQ"
      },
      "source": [
        "dca_df['days'] = day_maker(dca_df)"
      ],
      "execution_count": 96,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 225
        },
        "id": "u-o8wGFsXTGl",
        "outputId": "4f077ace-3e51-49e6-b29c-a3ef0ba65785"
      },
      "source": [
        "dca_df.head()"
      ],
      "execution_count": 97,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Rate (SCF/d)</th>\n",
              "      <th>days</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2004-04-01</th>\n",
              "      <td>2706039.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2004-05-01</th>\n",
              "      <td>2492086.2</td>\n",
              "      <td>30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2004-06-02</th>\n",
              "      <td>1816846.1</td>\n",
              "      <td>62</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2004-07-02</th>\n",
              "      <td>1920207.4</td>\n",
              "      <td>92</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2004-07-04</th>\n",
              "      <td>1683521.4</td>\n",
              "      <td>94</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            Rate (SCF/d)  days\n",
              "Date                          \n",
              "2004-04-01     2706039.0     0\n",
              "2004-05-01     2492086.2    30\n",
              "2004-06-02     1816846.1    62\n",
              "2004-07-02     1920207.4    92\n",
              "2004-07-04     1683521.4    94"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 97
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 383
        },
        "id": "uzLbo6MQXVwS",
        "outputId": "b7516178-5a1a-4ab0-e8ef-76c6be88e2ce"
      },
      "source": [
        "\n",
        "\n",
        "plt.figure(figsize=(12,4))\n",
        "plt.scatter(y=dca_df['Rate (SCF/d)'],x=dca_df['days'],marker='v',color='green')\n",
        "\n",
        "# plt.ylabel('Rate (SCF/d)')\n",
        "plt.grid()"
      ],
      "execution_count": 104,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1200x400 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "H0gp4qnbXgT4"
      },
      "source": [
        "### Step 2 : Hyperbolic Model Function & Curve Fitting methodology."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MZd1hUKeYhpx"
      },
      "source": [
        "from scipy.optimize import curve_fit"
      ],
      "execution_count": 105,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WrksBeHVXbGL"
      },
      "source": [
        "def q_hyp(t,qi,b,d):\n",
        "    \n",
        "    qfit = qi/(np.abs((1 + b * d* t))**(1/b))\n",
        "    \n",
        "    return qfit\n",
        "\n",
        "\n",
        "def hyp_fitter(q,t):\n",
        "        \n",
        "    #First we have to Normalize so that it converges well and quick.\n",
        "    q_n = q/max(q)\n",
        "    t_n = t/max(t)\n",
        "    \n",
        "    #curve-fit (optimization of parameters)\n",
        "    params = curve_fit(q_hyp,t_n,q_n)\n",
        "    [qi,b,d] = params[0]\n",
        "    \n",
        "    #These are for normalized t and q. \n",
        "    #We must re-adjust for q and t (non-normalized)\n",
        "    d_f = d/max(t)\n",
        "    qi_f = qi*max(q)\n",
        "    \n",
        "    #Now we can use these parameters. \n",
        "    q_hyp_fit = q_hyp(t,qi_f,b,d_f)\n",
        "    \n",
        "    return q_hyp_fit,params"
      ],
      "execution_count": 99,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gR7YSOyRXyrf"
      },
      "source": [
        "#This stepwise approach gives you a very nice picture about how linear regression is implemented. "
      ],
      "execution_count": 107,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ESUbyMt_XsOO"
      },
      "source": [
        "q = dca_df['Rate (SCF/d)'] ; t = dca_df['days']\n",
        "q_fit ,params = hyp_fitter(q,t)"
      ],
      "execution_count": 102,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 400
        },
        "id": "QJQ7QpJbXvWe",
        "outputId": "33fe99f3-3f0b-4068-cd61-5aeee2ecb9c1"
      },
      "source": [
        "plt.figure(figsize=(12,4))\n",
        "plt.scatter(t,q,marker='v',color='green',label='Actual Production Data')\n",
        "plt.plot(t,q_fit,color='brown',lw=4,label='DCA Model Fit')\n",
        "\n",
        "plt.ylabel(df.columns[0])\n",
        "\n",
        "plt.grid()\n",
        "plt.legend()"
      ],
      "execution_count": 103,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f66efc9d6d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 103
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1200x400 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Y1NEQlROY__U"
      },
      "source": [
        "### Notice that in this DCA technique (Physics base, Arps) we MUST know the equation prior to the project. We need that info otherwise we cannot do anything. \n",
        "\n",
        "### Whereas in Data Driven approaches, we normally start with ONLY DATA and fit the model that suits the best, use this model for Production Forecasting. \n",
        "\n",
        "### Best Suited Model for this : Time Series Forecasting (ARIMA/Prophet etc.)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bLtKFouzX4tb"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0puVtuE6cRJ_"
      },
      "source": [
        "# Hands on Excercise 2 : Machine Learning for Phi-K relationship modelling.\n",
        "\n",
        "> A Supervised Regression Problem"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rUKtHcK5e7eY"
      },
      "source": [
        "#### Step 1 : Import the Dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZCm7ub1scX8Y"
      },
      "source": [
        "df = pd.read_csv('https://raw.githubusercontent.com/Divyanshu-ISM/Machine-Learning-Deep-Learning/main/PhiK.csv',\n",
        "                 index_col=0)"
      ],
      "execution_count": 110,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        },
        "id": "6px6CL6rc2W8",
        "outputId": "21506449-7d56-4491-b153-35676bca7b4e"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 111,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Porosity</th>\n",
              "      <th>Swc</th>\n",
              "      <th>Permeability(D)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.269158</td>\n",
              "      <td>0.114209</td>\n",
              "      <td>2.042529</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.324275</td>\n",
              "      <td>0.072078</td>\n",
              "      <td>11.639989</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.218003</td>\n",
              "      <td>0.101849</td>\n",
              "      <td>1.015917</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.211875</td>\n",
              "      <td>0.099354</td>\n",
              "      <td>0.941715</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.322281</td>\n",
              "      <td>0.083444</td>\n",
              "      <td>8.452433</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Porosity       Swc  Permeability(D)\n",
              "0  0.269158  0.114209         2.042529\n",
              "1  0.324275  0.072078        11.639989\n",
              "2  0.218003  0.101849         1.015917\n",
              "3  0.211875  0.099354         0.941715\n",
              "4  0.322281  0.083444         8.452433"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 111
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "L2Spvts0e_Qp"
      },
      "source": [
        "#### Step 2 : Exploratory Data Analysis"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 405
        },
        "id": "pCies52Kc4H4",
        "outputId": "9c4887a0-e9cb-4e7c-846c-21211760e8d9"
      },
      "source": [
        "import seaborn as sns\n",
        "plt.style.use('default')\n",
        "\n",
        "plt.figure(figsize=(6,4))\n",
        "\n",
        "sns.kdeplot(df['Porosity'],label='Porosity',marker='*')\n",
        "sns.kdeplot(df['Swc'],label='Swc',marker='v')\n",
        "\n",
        "plt.xlim(min(df['Swc']), 0.6)\n",
        "\n",
        "plt.grid()\n",
        "\n",
        "plt.legend(loc='best')"
      ],
      "execution_count": 114,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f66e44f3550>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 114
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 600x400 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wgn97ulTfEFu"
      },
      "source": [
        "#### Step 3 : Train-Test split"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4sfF2K1FdGzB"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "X = df[['Porosity', 'Swc']]\n",
        "\n",
        "y = df['Permeability(D)']"
      ],
      "execution_count": 116,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7lDjaOV_dhz9"
      },
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.2,random_state=101)"
      ],
      "execution_count": 120,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XsI__XZUfJAP"
      },
      "source": [
        "#### Step 4 : Machine Learning Implementation : sklearn"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Zrb1RBySdSyv"
      },
      "source": [
        "from sklearn.tree import DecisionTreeRegressor"
      ],
      "execution_count": 117,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8b40iJyUdZpL"
      },
      "source": [
        "model = DecisionTreeRegressor(random_state=1)"
      ],
      "execution_count": 118,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mwgICRIBdbe4",
        "outputId": "7592b841-3c10-42bf-8184-1302f655c2b6"
      },
      "source": [
        "model.fit(X_train,y_train)"
      ],
      "execution_count": 121,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeRegressor(ccp_alpha=0.0, criterion='mse', max_depth=None,\n",
              "                      max_features=None, max_leaf_nodes=None,\n",
              "                      min_impurity_decrease=0.0, min_impurity_split=None,\n",
              "                      min_samples_leaf=1, min_samples_split=2,\n",
              "                      min_weight_fraction_leaf=0.0, presort='deprecated',\n",
              "                      random_state=1, splitter='best')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 121
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sKj9-UMDfP6w"
      },
      "source": [
        "#### 5. Step 5 : Model Predictions "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gyK3fN1MddDM",
        "outputId": "052e0f39-5e70-4c04-fd65-aafe5b757787"
      },
      "source": [
        "y.head()"
      ],
      "execution_count": 124,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0     2.042529\n",
              "1    11.639989\n",
              "2     1.015917\n",
              "3     0.941715\n",
              "4     8.452433\n",
              "Name: Permeability(D), dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 124
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bIl5593wdnfi",
        "outputId": "0f363221-0846-433d-e0df-f1b62e3b8d16"
      },
      "source": [
        "print(model.predict(X.head()))"
      ],
      "execution_count": 123,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[ 2.04252923 11.69904489  1.02501243  0.94171488  8.45243294]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oATbp2mhfWsC"
      },
      "source": [
        "#### Performance evalauation : Visual and Quantitative."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5XcjkYOJdpYS"
      },
      "source": [
        "from sklearn.metrics import mean_squared_error as mse"
      ],
      "execution_count": 127,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Wqtc7YUHdyzS"
      },
      "source": [
        "y_p = model.predict(X_test)"
      ],
      "execution_count": 132,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 467
        },
        "id": "7MnKgGKMd4xZ",
        "outputId": "736878b9-96c0-4eed-f9c1-c414df5259d3"
      },
      "source": [
        "plt.grid()\n",
        "plt.scatter(y_p,y_test,marker='X',label='Model Performance')\n",
        "\n",
        "plt.plot(y_test,y_test, color='red',label='Idel Performance')\n",
        "\n",
        "plt.xlabel('Predicted K values')\n",
        "plt.ylabel('Actual K values')\n",
        "\n",
        "plt.legend()"
      ],
      "execution_count": 136,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f66df90fa10>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 136
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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exalTp9ChQwd06tQJa9euhYeHR3U0iRBSg8xNjBHY3JEfzuY4DoHNHet2MjN5MtClS8nxu+/KpwDX4tXcOcZe43nhOiAnJwd2dnbIzs6Gra2t4FxRUREeP34sWMdEXVqfQ1OLGUpb9a2d5b2/JRIJjh07hoEDByrNC9I3htJWaqf+0Vlb8/OBsnMrT50C+vatlperqJ0VfX6rQnNoCCGEEAJERQE9ewrLcnIAG9Xr7NQ2df/PUEIIIYS8nnHjhMnM2LHyIaY6kswA1ENDCCGEGK7cXKDscM65c8C/T8nWJdRDQwghhBiiyEjlZCYvr04mMwAlNIQQQojhef99oF+/kuNPPpEPMWlx8dOaRkNOhBBCiKHIygLq1xeWRUcDnTvrJh4toh4aQgghxBBERCgnMwUFepHMAJTQEEIIIfpvyBBg8OCS42nT5ENMlexDV5fQkBMhhBCir168ABo0EJZdvQpUwzYyukY9NESlc+fOgeM4ZGVlqX2Pp6cnvi+9G2sN+umnn+Dm5gYjIyOdxUAIIbXKoUPKyUxRkV4mMwAlNHXSuHHjwHEcPv30U6VzU6ZMAcdxGDduXM0HVolFixaB4zhwHAcTExN4enpixowZyMvLe616c3JyEBYWhrlz5+Lp06eYNGmSliImhJA6qndvYMSIkuMvv5QPMZmb6y6makYJzWsSFUsRk5gJxZZYjDHEJGZCVKzZDtOacnNzw969e1FYWMiXFRUVITw8XGsbTlaH1q1bIzU1FUlJSVi1ahV++uknzJo1q0p1McZQXFyMlJQUSCQSDBo0CK6urrC0tKxSfRKJpEr3EUJIrZGeDnAccOZMSdnNm8CKFbqLqYZQQvMaxMUyfLL7OoK3XMaSo/GQyRgWH4lH8JbLmLQrrlqTGn9/f7i5ueHgwYN82cGDB+Hu7g4/Pz/BtSKRCJ9//jmcnZ1Rr149dO3aFVevXhVcc+zYMXh7e8PCwgJvv/02kpKSlF7z4sWL6NatGywsLODm5obPP/8c+fn5GsVtYmICFxcXNGnSBKNGjcKHH36IP/74A4B8c8gVK1agadOmsLCwQPv27bF//37+XsUw2PHjxxEQEABzc3P88ssvaNu2LQCgWbNm4DiOj33jxo1o3rw5zMzM0LJlS+zevVsQC8dx2LhxI4YOHQorKyssX74cK1euhL+/P7Zt2wZ3d3dYW1vjs88+g1QqxerVq+Hi4gJnZ2csW7ZMUNd3332Htm3bwsrKCm5ubvjss88EPU87duyAvb09Tp48CR8fH1hbW6N///5ITU0V1LNt2za0bt0a5ubmcHV1RVhYGH8uKysLEydOhJOTE2xtbdGrVy/89ddfGn3/CSF6bO9eoGHDkmMzM0AsBtq3111MNYgSmioSFUsx/UACLj7MBABsj07CoB8vYEdMEgDgwoOMak9qxo8fj+3bt/PH27ZtQ2hoqNJ1c+bMwYEDB7Bz505cv34dLVq0QFBQEF6+fAkAePLkCUaMGIEhQ4bg5s2bmDhxIr788ktBHY8fP8bAgQMxcuRI3Lp1C//73/9w8eJFwQduVVhYWEAsFgMAVqxYgV27dmHTpk24e/cuZsyYgY8++ghRUVGCe7788kusXLkSCQkJ6Nu3L06fPg0AuHLlClJTU+Hm5oZDhw5h2rRpmDVrFu7cuYNPPvkEoaGhOHv2rKCuRYsW4Z133sHt27f5711iYiKOHz+OEydO4Ndff8XWrVsxaNAg/PPPP4iKisKqVavw9ddf4/Lly3w9RkZG+O9//4u7d+9i586dOHPmDObMmSN4rYKCAnz77bfYvXs3zp8/j5SUFHzxxRf8+Y0bN2LKlCmYNGkSbt++jT/++AMtWrTgz7/33ntIT0/H8ePHERcXB39/f/Tu3Zv/ORJCDBRjQGAgMHp0SdnixYBIBOj5ruQCTM9lZ2czACw7O1vpXGFhIYuPj2eFhYUa13vxfjrzmHu00q+Yh5naaIbA2LFj2bBhw1h6ejozNzdnSUlJLCkpidWrV49lZGSwYcOGsbFjxzLGGMvLy2OmpqZsz549/P1isZg1atSIrV69mjHG2Lx585ivr6/gNebOncsAsFevXjGpVMrGjBnDPv74Y8E1Fy5cYEZGRvz3z8PDg61du7bcuBcuXMjat2/PH1+7do01aNCAvfvuu6yoqIhZWlqymJgYwT0TJkxgo0ePZowxdvbsWQaA/f7774Jrbty4wQCwx48f82WdO3dWive9995jAwcO5I8BsOnTp/PHUqmUzZ07l1laWrKcnBy+PCgoiHl6ejKpVMqXtWzZkq1YsaLctu7bt485Ojryx9u3b2cA2MOHD/my9evXs4YNG/LHjRo1Yv/3f/+nsr4LFy4wW1tbVlRUJChv3rw527x5s8p7ynt/i8Vi9vvvvzOxWFxu/PrCUNpK7dQ/arf12TPG5ClNydedOzUTpBZU1M6KPr9Voce2q6hTMweMDnDFr3Gp5V4T2sUTnZo5VFsMTk5OGDRoEHbs2AHGGAYNGoQGZWa0JyYmQiKRoEuXLnyZqakp3nrrLSQkJAAAEhIS0LFjR8F9gYGBguM7d+7g7t27CA8P58sYY5DJZHj8+DF8fHzUivn27duwtraGVCqFWCzGoEGDsG7dOjx8+BAFBQXo27ev4HqxWKw0hPamGjP0ExISlCYHd+nSBT/88EOldXl6esKm1A6zDRs2hLGxMYyMjARl6enp/PHp06exYsUK/P3338jJyUFxcTGKiopQUFDAz+mxtLRE8+bN+XtcXV35OtLT0/Hs2TP07t1bZXv++usv5OXlwdHRUVBeWFiIxMTECr8XhBA9tXOnfJdsBXt7ICMDMDHMj3bDbLUWcByH2X2a4ubTPCSk5Sqd93G1wfxBvuA4rlrjGD9+PD/ss379+mp7nfz8fEyaNAnTpk1TOqfJJOSWLVvijz/+gImJCRo1agQzMzMA4Oe9REREoHHjxoJ7zMvMyrfS4l4jquoyLdNFy3GcyjKZTAZAHvvgwYMxefJkLFu2DA4ODrh48SImTJgAsVjMJzSq6mD/Tia3qGRxq7y8PLi6uuLcuXNK5+zt7StuJCFEvzAGvPEGcOtWSdnKlcDcubqLqRaghKaKGGP4z+nHKpMZAEhIzcXSiHgsGFy9SU3//v0hFovBcRyCgoKUzismxUZHR8PDwwOA/Gmeq1evYvr06QAAHx8ffmKuQmxsrOC4Xbt2SEhIEMzpqAozMzOVdfj6+sLc3BwpKSnooYWdXn18fBAdHY2xY8fyZdHR0fD19X3tusuKi4uDTCbDmjVr+F6c3377TaM6bGxs4OnpiT///BNvv/220nl/f3+kpaXxj7sTQgzUkydA2T8i790DvL11E08tQglNFcU+elnhcBMgnyjcz9cFgc0dK7zudRgbG/NDR8bGxkrnraysMHnyZMyePRsODg5wd3fH6tWrUVBQgAkTJgAAPv30U6xZswazZ8/GxIkTERcXhx07dgjqmTZtGvr164ewsDBMnDgRVlZWiI+PR2RkJNatW/fa7bCxscEXX3yBGTNmQCaToWvXrsjOzkZ0dDRsbW0FiYk6Zs+ejffffx9+fn7o06cPjhw5goMHD/ITiLWpRYsWkEgk+PHHHzFkyBBER0dj06ZNGtezaNEifPrpp3B2dsaAAQOQm5uL6OhoTJ06FX369EFgYCCGDx+O1atXw9vbG8+ePUNERATeeecdtYbhCCF13JYtQOmhdFdXeYKj4ne/IdLpU04bN25Eu3btYGtrC1tbWwQGBuL48eP8+Z49e/ILsSm+VC0mpwv+Hvbo3NQeRqU6X3xcS+ZdGHFAD28n+HtU/3CA4vtXnpUrV2LkyJEYM2YM/P398fDhQ5w8eRL1/92kzN3dHQcOHMDvv/+O9u3bY9OmTVi+fLmgjjZt2uDs2bO4f/8+unXrBj8/PyxYsACNGjXSWjuWLl2K+fPnY8WKFfDx8UH//v0RERGBpk2balzX8OHD8cMPP+Dbb79F69atsXnzZmzfvh09e/bUWrwK7du3x3fffYdVq1ahTZs22LNnD1ZUYc2HsWPH4vvvv8eGDRvQunVrDB48GA8ePAAgH546duwYunfvjtDQUHh7e+ODDz5AcnIyGpZ+TJMQon8Yk/fAlE5mvv8eePaMkplSOKYYxNeBI0eOwNjYGF5eXmCMYefOnfjPf/6DGzduoHXr1ujZsye8vb2xZMkS/h5LS8sKP7zLysnJgZ2dHbKzs5XuKyoqwuPHj9G0aVPUq1dPo9hlMhkyX2bhi8MPcP5BJkK7eGL+IF8sORqPHTFJ6OHthJ9CAmBuUvffbDKZDDk5ObC1tRVMjNU3+tbO8t7fEokEx44dw8CBA5Xm9egbQ2krtVP/8G318YFpy5bCk4mJQLNmuglMyyr6mVb0+a2KToechgwZIjhetmwZNm7ciNjYWLRu3RqAPIFxcXHRRXiVMjMxwuYx/rj5JAedmjmA4zgsHOKLoNYu8Pew14tkhhBCiG40PXoUpsOHlxS0aCGfL6MHf3BVh1rzXZFKpdi7dy/y8/MFjwzv2bMHDRo0QJs2bTBv3jwUFBToMEpl5ibGCGzuyE/85TgOgc0dKZkhhBBSNTIZTDw90e7nn0vKNm0CHjygZKYCOp8UfPv2bQQGBqKoqAjW1tY4dOgQ/yRKcHAwPDw80KhRI9y6dQtz587FvXv3BMv9lyUSiSASifjjnJwcAPJurbJ79UgkEn4tFcUjuOpipfZu0vTeusZQ2qpv7ZTJZGCMQSKRCCaMK/4fGMLeVYbSVmqnHnnwAKatW6P0s7GShw/lTzbpYbsr+plq+nPW6RwaQL5wWkpKCrKzs7F//378/PPPiIqKUvl47ZkzZ9C7d288fPhQsEBZaYsWLcLixYuVysPDw5U2LVTsK+Tm5savh0KIvhCLxXjy5AnS0tJQXFys63AIIZVocegQWu/cyR9ne3ri3Nq18s0mDVBBQQGCg4PVnkOj84SmrD59+qB58+bYvHmz0rn8/HxYW1vjxIkTKtdcAVT30Li5uSEzM1PpGyISiZCSkgIPD49KFzYrizGG3Nxc2NjYVPviebpmKG3Vt3YWFhYiOTkZ7u7ugsUJJRIJIiMj0bdvX4OYWGkIbaV21nFSKUxcXcFlZfFFok2bcMLFRf/aWkZFP9OcnBw0aNCgbkwKVkUmkwkSktJu3rwJQL5kfHnMzc2VVpYF5Ku0lv1mGRkZgeM4FBUVabz6rGJIguM4vXgipiKG0lZ9a2dRURE4joOFhYXKNYpU/Z/QV4bSVmpnHRQfD/z7EAzv6VMYOTkBx47pV1sroKqdmrZbpwnNvHnzMGDAALi7uyM3Nxfh4eE4d+4cTp48icTERISHh2PgwIFwdHTErVu3MGPGDHTv3h3t2rXTyusbGxvD3t6e30/H0tJS7b/MZTIZxGIxioqK9OLDryKG0lZ9aSdjDAUFBUhPT4e9vb3KZIYQUgt88w0wf37JcceOwKVL8iEmPZwvU910mtCkp6cjJCQEqampsLOzQ7t27XDy5En07dsXT548wenTp/H9998jPz8fbm5uGDlyJL7++mutxqB4JLz0RoPqYIyhsLAQFhYWejE8URFDaau+tdPe3r7WLnlAiEGTSAAbG6D0aER4ODB6tO5i0gM6TWi2bt1a7jk3NzdERUVVewwcx8HV1RXOzs4azaiWSCQ4f/48unfvrvfdgYbSVn1qp6mpKfXMEFIb3boFtG8vLHv+HHB21k08eqTWzaHRFWNjY40+AIyNjVFcXIx69erV+Q+/yhhKWw2lnYQQHfm//wNKbyvz9tvAmTO6i0fPUEJDCCGEVCexGCj7sMr+/cDIkbqJR09RQkMIIYRUl2vXgA4dhGWZmYCjo27i0WN191EOQgghpDabNUuYzAwaJN85m5KZakE9NIQQQog2FRUBZRdrPXIEGDxYN/EYCEpoCCGEEG25dAno3FlY9uoVYG+vm3gMCA05EUIIIdrw2WfCZObdd+VDTJTM1AjqoSGEEEJeR0EBUHb7nJMngX79dBOPgaKEhhBCCKmq8+eBHj2EZdnZgBqbKRLtoiEnQgghpCpCQ4XJzJgx8iEmSmZ0gnpoCCGEEE3k5ionLWfPAj176iQcIkc9NIQQQoi6Tp9WTmby8pJSmo4AACAASURBVCiZqQUooSGEEELU8cEHQN++JceTJsmHmMpOCCY6QUNOhBBCSEWys5Ufvb54EejSRTfxEJWoh4YQQggpz7FjyslMQQElM7UQJTSEEEKIKsOGyfdfUpg2TT7EVHZbA1Ir0JATIYQQUtrLl8obSF65orxrNqlVqIeGEEIIUfj9d+VkpqiIkpk6gBIaQgghBJA/wfTOOyXHX34pH2IyN9ddTERtNORECCHEsGVkAM7OwrIbN4A33tBNPKRKqIeGEEKI4frtN2EyY2ICiMWUzNRBlNAQQggxPIzJH70eNaqkbNEiQCIBTE11FhapOhpyIoQQYlhSU4FGjYRld+4ArVvrJh6iFdRDQwghxHDs2iVMZmxt5b0ylMzUeZTQEEII0X+MAX5+wNixJWUrVsi3NTChwQp9QD9FQggh+u2ffwA3N2HZ338DLVvqJh5SLaiHhhBCiP7askWYzLi4AMXFlMzoIUpoCCGE6B/G5EnLpEklZWvXyicEGxvrLi5SbWjIiRBCiH5JSgKaNhWWPXwING+uk3BIzaAeGkIIIfpj3TphMtOsGSCVUjJjAKiHhhBCSN0nkwEeHvIJwAobNgCTJ+suJlKjKKEhhBBStz18CHh5CcuSkwF3d93EQ3SChpwIIYTUXd9+K0xm2rSR99ZQMmNwdJrQbNy4Ee3atYOtrS1sbW0RGBiI48eP8+eLioowZcoUODo6wtraGiNHjsTz5891GDEhhJBaQSoFHB2B2bNLyrZuBW7fBjhOd3ERndFpQtOkSROsXLkScXFxuHbtGnr16oVhw4bh7t27AIAZM2bgyJEj2LdvH6KiovDs2TOMGDFClyETQgjRtYQE+eq+L1+WlD19Cowfr7uYiM7pdA7NkCFDBMfLli3Dxo0bERsbiyZNmmDr1q0IDw9Hr169AADbt2+Hj48PYmNj0alTJ12ETAghRIe8f/sNpuHhJQVvvQXExlKvDKk9k4KlUin27duH/Px8BAYGIi4uDhKJBH369OGvadWqFdzd3XHp0qVyExqRSASRSMQf5+TkAAAkEgkkEonW4lXUpc06aytDaSu1U/8YSlsNop0SCUwcHeFTVMQXFe/cCTZ6tHzlXz1jED9TVNxOTdvOMcaYVqKqotu3byMwMBBFRUWwtrZGeHg4Bg4ciPDwcISGhgqSEwB466238Pbbb2PVqlUq61u0aBEWL16sVB4eHg5LS8tqaQMhhJDqY5uUhLenTxeUndixAyJ7ex1FRGpCQUEBgoODkZ2dDVtb20qv13kPTcuWLXHz5k1kZ2dj//79GDt2LKKioqpc37x58zBz5kz+OCcnB25ubujXr59a3xB1SSQSREZGom/fvjA1NdVavbWRobSV2ql/DKWt+txOo4ULYbxiBX+c0aYNrC5fRm89a2dZ+vwzLa2idipGWNSl84TGzMwMLVq0AAAEBATg6tWr+OGHHzBq1CiIxWJkZWXBvlQW/vz5c7i4uJRbn7m5OczNzZXKTU1Nq+VNUV311kaG0lZqp/4xlLbqVTvFYqDM7/LiX39FjIUFBupTOyuhVz/TCqhqp6btrnXr0MhkMohEIgQEBMDU1BR//vknf+7evXtISUlBYGCgDiMkhBBSreLilJIZZGSAjRypm3hInaDTHpp58+ZhwIABcHd3R25uLsLDw3Hu3DmcPHkSdnZ2mDBhAmbOnAkHBwfY2tpi6tSpCAwMpCecCCFEX33xBbBmTcnxgAHAsWPyf+v5BFnyenSa0KSnpyMkJASpqamws7NDu3btcPLkSfTt2xcAsHbtWhgZGWHkyJEQiUQICgrChg0bdBkyIYSQ6lBUBFhYCMsOHwaGDtVNPKTO0WlCs3Xr1grP16tXD+vXr8f69etrKCJCCCE1LjYWKDuV4OVLoH593cRD6qRaN4eGEEKIAQkLEyYzI0YAjFEyQzSm86ecCCGEGKCCAsDKSlh24gQQFKSbeEidRz00hBBSS4iKpYhJzIRivVPGGGISMyEqluo4Mi27cEE5mcnOpmSGvBZKaAghpBYQFUsxaVccgrdcxpKj8ZDJGBYfiUfwlsuYtCtOf5KaCROA7t1Ljj/6SD7EpMWFT4lhoiEnQgjRMUUyc+FBBgBge3QSYh+9QEJqLgDgwoMMTNoVh59CAmBuYqzLUKsuLw+wsRGWnTkDvP22buIheod6aAghRMfikl8h6n4GZKV21lMkMwAgY0DU/QxcT87SQXRa8OefyslMbi4lM0SrKKEhhBAdC2zmiHGdPSu8JrSLJzo1c6iZgLQpOBjo06fk+OOP5UNM1ta6i4noJRpyIoQQHeM4DgsG++Ly4xeCnhkFH1cbzB/kC47jdBBdFWVnA2V3w75wAejaVTfxEL1HPTSEEKJjjDEsORqvMpkB5MNPSyPi+aefar3jx5WTmYICSmZItaKEhhBCdOzSoxfYEZNU4TXyicIvayag1zF8ODBwYMnx1KnyIaay2xoQomU05EQIIToW4FEfPbydcOFBycRgH1cbvsfGiAO6eTnB38O+glp07OVLwNFRWHb5MvDWW7qJhxgc6qEhhBAdMzcxxk8hAejm5QRAPgE4Ymo3fqJwNy+n2v3I9uHDyslMURElM6RGUQ8NIYTUAoqk5npyFjo1cwDHcVg4xBdBrV3g72Ffe5OZfv2AyMiS4zlzgFWrdBcPMViU0BBCSC1hbmKMwOYlPR0cxwmOa5WMDMDZWVh2/Trg56ebeIjBoyEnQgghmvntN2EyY2wMiESUzBCdooSGEEKIehgDunUDRo0qKVu4ECguBszMdBcXIaAhJ0IIIepISwNcXYVlt28DbdroJh5CyqAeGkII0ZCoWIqYxEx+oTvGGGISM/VnR+yydu8WJjPW1oBEQskMqVUooSGEEA0odsYO3nIZS47GQyZjWHwkHsFbLmPSrjj9SmoYAwICgJCQkrLly+UbS5pQBz+pXegdSQghalIkMxceZABQrN5bsv/ShQcZmLQrrnavGaOuf/4B3NyEZX//DbRsqZt4CKkE9dAQQoia4pJfIep+yWq+AAT7L8kYEHU/A9eTs3QQnRZt3SpMZpyd5RN/KZkhtRglNIQQoqbAZo786r3lCe3iiU7NHGomIG1jDPDxASZOLCn77jvg+XP5o9mE1GI05EQIIWriOA4LBvvi8uMXKnfG9nG1wfxBvuA4TgfRvabkZMDTU1j28CHQvLlOwiFEU9RDQwghamKMYcnReJXJDCAffloaEc8//VRnrF8vTGaaNgWkUkpmSJ1CCQ0hhKjp0qMX2BGTVOE18onCL2smoNclkwEeHkBYWEnZhg3Ao0eAEX08kLqF3rGEEKKmAI/66OHtBKNSI0o+rjb8v404oIe3E/w97HUQnYYePpTPi0lJKSlLSgImT9ZZSIS8DkpoCCFETYodsbt5OQGQTwCOmNqNnyjczcupbjyyvWYN4OVVcuzrW9JbQ0gdRZOCCSFEA4qk5npyFjo1cwDHcVg4xBdBrV3g72Ffu5MZqRRwcQEyM0vKtm4Fxo/XXUyEaAklNIQQoiFzE2MENnfkjzmOExzXSn//LX8ku7R//gEaN9ZNPIRoGQ05EUKIvlu+XJjMvPmmfIiJkhmiR6iHhhBC9FVxMWBnBxQUlJTt3g189JHuYiKkmlBCQwgh+uj2baBdO2FZaqp8Dg0hekinQ04rVqxAhw4dYGNjA2dnZwwfPhz37t0TXNOzZ09wHCf4+vTTT3UUMSGE1AHz5wuTme7d5UNMlMwQPabTHpqoqChMmTIFHTp0QHFxMb766iv069cP8fHxsLKy4q/7+OOPsWTJEv7Y0tJSF+ESQkjtJhYDZmbCst9+A957TzfxEFKDNE5oTpw4AWtra3Tt2hUAsH79emzZsgW+vr5Yv3496tevr1Fdpe3YsQPOzs6Ii4tD9+7d+XJLS0u40F8WhBBSLrvERJgOHy4szMgAGjTQTUCE1DCNh5xmz56NnJwcAMDt27cxa9YsDBw4EI8fP8bMmTNfK5js7GwAgIODcKfaPXv2oEGDBmjTpg3mzZuHgtIT3AghxMAZffkles6aVVIQFCTfOZuSGWJANO6hefz4MXx9fQEABw4cwODBg7F8+XJcv34dAwcOrHIgMpkM06dPR5cuXdCmTRu+PDg4GB4eHmjUqBFu3bqFuXPn4t69ezh48KDKekQiEUQiEX+sSL4kEgkkEkmV4ytLUZc266ytDKWt1E79o/dtLSqCqa0tSi/lV7x/P9jQoYAetlnvf56lGEpbK2qnpm3nmIbbwjo4OODixYvw9fVF165dERISgkmTJiEpKQm+vr5V7j2ZPHkyjh8/josXL6JJkyblXnfmzBn07t0bDx8+RHMVO8EuWrQIixcvVioPDw+nuTeEEL1R//59dJ8zR1B27JdfILG21lFEhGhXQUEBgoODkZ2dDVtb20qv1zihGTp0KMRiMbp06YKlS5fi8ePHaNy4MU6dOoWwsDDcv39f46DDwsJw+PBhnD9/Hk2bNq3w2vz8fFhbW+PEiRMICgpSOq+qh8bNzQ2ZmZlqfUPUJZFIEBkZib59+8LU1FRr9dZGhtJWaqf2iIuluPEkC295yrcGYIzhStJL+LnZw6wGtwbQ15+p0fTpMN6wgT8uHjIEERMm6F07y9LXn6cqhtLWitqZk5ODBg0aqJ3QaDzktG7dOnz22WfYv38/Nm7ciMb/rjR5/Phx9O/fX6O6GGOYOnUqDh06hHPnzlWazADAzZs3AQCurq4qz5ubm8Pc3Fyp3NTUtFreFNVVb21kKG2ldr4eUbEUn/16A1H3MxDaxRPzB/liydF47IhJQg9v3WzeqDc/04ICoNQToACA48fBevcGjh3Tn3ZWwlDaCRhOW1W1U9N2a5zQuLu74+jRo0rla9eu1bQqTJkyBeHh4Th8+DBsbGyQlpYGALCzs4OFhQUSExMRHh6OgQMHwtHREbdu3cKMGTPQvXt3tCu7YBQhROdExVJM2hWHCw8yAADbo5MQ++gFElJzAQAXHmRg0q64urEjdW1z8SLQrZuwLCtLvhKwns+zIEQdVVpYLzExEV9//TVGjx6N9PR0APIemrt372pUz8aNG5GdnY2ePXvC1dWV//rf//4HADAzM8Pp06fRr18/tGrVCrNmzcLIkSNx5MiRqoRNCKlmccmvEHU/A7JSA9mKZAYAZAyIup+B68lZOoiuDps4UZjMBAfLn2Kys9NdTITUMhr30ERFRWHAgAHo0qULzp8/j2XLlsHZ2Rl//fUXtm7div3796tdV2XTd9zc3BAVFaVpiIQQHQls5ohxnT2xIyap3GtCu3iiUzOHcs+TUvLyABsbYdmffwK9eukmHkJqMY17aL788kt88803iIyMhFmpFSl79eqF2NhYrQZHCKlbOI7DgsG+8HG1UXnex9UG8wf5guO4Go6sDjpzRjmZyc2lZIaQcmic0Ny+fRvvvPOOUrmzszMyMzO1EhQhpG5ijGHJ0XjBMFNpCam5WBoRX2nvrKESFUsRk5gJ9tFHQO/efLk0dLx8iIkeySakXBonNPb29khNTVUqv3HjBv/EEyHEMF169KLC4SZAMVH4Zc0EVIeIiqWYtikKnVs4gduzhy9/L3glxnf9BKJiqQ6jI6T20zih+eCDDzB37lykpaWB4zjIZDJER0fjiy++QEhISHXESAipIwI86qOHtxOMSo0olR5+MuKAHt5O8Pew10F0tZeoWIofZ6/Dpqm9BeUtZx7AVbc2/NNhlNQQUj6NE5rly5ejVatWcHNzQ15eHnx9fdG9e3d07twZX3/9dXXESAipI8xNjPFTSAC6eTkBkE8AjpjaDeM6ewIAunnpZh2a2i5v0DB88f10/niH/2B4zj0Kkal8TS16OoyQymn8lJOZmRm2bNmC+fPn486dO8jLy4Ofnx+8vLyqIz5CSB2jSGquJ2ehUzP5SsELh/giqLUL/D3sKZkp7dUrwMEBjqWKho9Zg5uNWipdSk+HEVIxjRMaBXd3d7i7u2szFkKInjA3MUZg85KPaY7jBMcEwB9/AMOGCYqG/ucUbmWKlS6lp8MIqZzGCc348eMrPL9t27YqB0MIIQZhwADgxAn+kH3xBRZ3G4db5UyoVjwdtmAwJTWElEfjhObVq1eCY4lEgjt37iArKwu9aH0EQggpX2Ym4OQkLIuLwyU7d+zYcrnCW7dHJ6Gfrwv1dBFSDo0TmkOHDimVyWQyTJ48Gc2bN9dKUIQQonf27QPef19YJhIBZmYIKJaih7cTLjwo2TbCx9WGX8/HiJNPqKanwwgpX5X2clKqxMgIM2fOrNIGlYQQotcYA3r0ECYz8+fLy/9dbZ2eDiPk9VV5UnBZiYmJKC4u1lZ1hBBS96WlAa6uwrJbt4C2bZUupafDCHk9Gic0M2fOFBwzxpCamoqIiAiMHTtWa4ERQkid9ssvwJgxJcfW1vLHtE3K/7VLT4cRUnUaJzQ3btwQHBsZGcHJyQlr1qyp9AkoQgjRe4wBHToAcXElZcuWAV99pbuYCDEAGic0Z8+erY44CCGk7nv6FGjSRFiWkAC0aqWbeAgxIFqZFEwIIQZv2zZhMuPkBBQXUzJDSA1Rq4fGz89P7cWcrl+//loBEUJIncIY0Lq1vCdGYc0aoMx8Q0JI9VIroRk+fHh1x0EIIXVPcjLg6Skse/AAaNFCJ+EQYsjUSmgWLlxY3XEQQkjdsmEDMGVKybG7O/D4MWBEI/mE6AL9zyOEEE3IZEDTpsJkZt06eW8NJTOE6IzG//ukUim+/fZbvPXWW3BxcYGDg4PgixBC6gpRsRQxiZlgTL7fAGMMMYmZEBVLVd+QmAgYGwNJSSVljx8LkxtCiE5onNAsXrwY3333HUaNGoXs7GzMnDkTI0aMgJGRERYtWlQNIRJCiPaJiqWYtCsOwVsuY8nReMhkDIuPxCN4y2VM2hWnnNSsXSucG+PjI++tKTuHhhCiExonNHv27MGWLVswa9YsmJiYYPTo0fj555+xYMECxMbGVkeMhBCiVYpk5sKDDADynawH/XgBO2KSAAAXHmSUJDVSKdCwofCppS1bgPh4QM2nPwkh1U/jhCYtLQ1t/92HxNraGtnZ2QCAwYMHIyIiQrvREUJINYhLfoWo+yU7WwPgd7YGABkDou5nIP7MVflWBenpJRc+eQJMnFiD0RJC1KFxQtOkSROkpqYCAJo3b45Tp04BAK5evQpzc3PtRkcIIdUgsJkjv5N1ebY+i4RfUGBJQUCAfIip7ErAhJBaQeOE5p133sGff/4JAJg6dSrmz58PLy8vhISE0F5OhJA6geM4LBjsi1YuNkrnjGVSJKx9D713/1BSuHs3cO0aDTERUotpvJfTypUr+X+PGjUKHh4eiImJgZeXF4YMGaLV4AghpDowxrDwyF38nZYrKPfOSMKpbWHCi1NTAReXGoyOEFIVGic0RUVFqFevHn/cqVMndOrUSatBEUJIdTr/IAO7LyULymZc2INpMb/yx1ea+KLo9Bl0d2lY0+ERQqpA4yEnZ2dnjB07FpGRkZDJZNUREyGEqE3jtWTKMJEWI3H1UEEyM2XoXLz/4WqAo4XyCKkrNP7funPnThQUFGDYsGFo3Lgxpk+fjmvXrlVHbIQQUqHK1pIRl5PUdPdywphOHmj9PBEPvx0OY1byx5n/1D2I8OmGkEAPdPNqUFNNIYS8pipNCt63bx+eP3+O5cuXIz4+Hp06dYK3tzeWLFlSHTESQogSddaS+XzvTZX3chyHJTG7ELFjGl923tMPnnOP4qWlHVq6WGPRkNbgaBIwIXVGlftTbWxsEBoailOnTuHWrVuwsrLC4sWLtRkbIYSUS521ZC4+zFS+USQCOA7cf/7DF3084muEjFrKH99Ly8PSiHh+GIsQUvtVOaEpKirCb7/9huHDh8Pf3x8vX77E7NmztRkbIYSUS521ZD7q6CEsuHIFKPVQAwC0//xXRHopP9iwPToJsY9evm6YhJAaonFCc/LkSYwdOxYNGzbE5MmT0bBhQ5w6dQrJycmCR7rVsWLFCnTo0AE2NjZwdnbG8OHDce/ePcE1RUVFmDJlChwdHWFtbY2RI0fi+fPnmoZNCNEzirVkfFyV15IBAB9XG8wJallS8PnnQMeO/GHcG93hOfcosi3k97dysRbc361FA/h72Gs/cEJItajSHJrCwkLs2rULaWlp2Lx5M7p3716lF4+KisKUKVMQGxuLyMhISCQS9OvXD/n5+fw1M2bMwJEjR7Bv3z5ERUXh2bNnGDFiRJVejxCiPxhjWHI0XjDMVFpCai5Wn7wHI5EIpmZmwI8/8uckR47g+7DVgus5lJkvQ9NnCKlTNF6H5vnz57CxUf0XkaZOnDghON6xYwecnZ0RFxeH7t27Izs7G1u3bkV4eDh69eoFANi+fTt8fHwQGxtL698QUoNExVLEJb9CYDNHcBwHxhguPXqBAI/6MDcxrvF4Lj16wU8ALk/8/uOYv3uusDArC1czJbhw8bKgOKHMInsXHmTienIWAps7aiNcQkg10zih0VYyo4pio0sHBwcAQFxcHCQSCfr06cNf06pVK7i7u+PSpUsqExqRSASRSMQf5+TkAAAkEgkkEonWYlXUpc06aytDaSu1s3ziYik+33sTFx9m4qOOHpgT1BKrTtzDnivJ6NqiAf77wRswq+Gkpn0ja/Ru6YiYxEx+YnDLhja491yemCw9tg7v3zzJXy8bNQrS3bsBAG+6MYwPdMeeK8lK9Sp81NEDAW42deL9QO9d/WMoba2onZq2nWO1ZBq/TCbD0KFDkZWVhYsXLwIAwsPDERoaKkhQAOCtt97C22+/jVWrVinVs2jRIpVPW4WHh8PS0rJ6gieE1BrGRUUY/MEHgrLoxYuR2b69jiIihFRFQUEBgoODkZ2dDVtb20qv17iHprpMmTIFd+7c4ZOZqpo3bx5mzpzJH+fk5MDNzQ39+vVT6xuiLolEgsjISPTt2xempqZaq7c2MpS2UjtVu/z4BSbsrHzxzG1jO+Ctpg7aCFEj4mIpbj7JRgfP+jCKioLJ8OGC80d//RW9hg4VtJUxhpXH71XaQzO3f8s6sRYNvXf1j6G0taJ2KkZY1KXVhCYvLw/W1taVX1hGWFgYjh49ivPnz6NJkyZ8uYuLC8RiMbKysmBvX/K0wfPnz+FSzmZx5ubmMDc3Vyo3NTWtljdFddVbGxlKW6mdQl28GmJ0x6YVzlcJ7eKJzl7OOvnwNzU1RRfvesCYMcAvv5QKKhSSzZshPXZMqa0xiZnYdikFFc383RqTgj6tG9WpOTT03tU/htJWVe3UtN1qP+W0du3aCs/n5uYiKChIoxdnjCEsLAyHDh3CmTNn0LRpU8H5gIAAmJqa4s8//+TL7t27h5SUFAQGBmr0WoSQqlHn8ej5g3x115ORkwNwnCCZYVFRiPm/1eVufRDgUR89vJ1gVCrk0u0z4oAe3k702DYhdYjaCc1XX32FXbt2qTyXn5+P/v3748WLFxq9+JQpU/DLL78gPDwcNjY2SEtLQ1paGgoLCwEAdnZ2mDBhAmbOnImzZ88iLi4OoaGhCAwMpCecCKkh6jwerbNVdU+eBOzsBEWy3DwsznJE8JbL/NYHlx+/EGxeGZf8CuuC/dDNywmAvIcpYmo3fqG+bl5O+CkkQCdPbxFCqkbtIafdu3djzJgxsLe3x9ChQ/ny/Px8BAUFISMjA1FRURq9+MaNGwEAPXv2FJRv374d48aNAyDvGTIyMsLIkSMhEokQFBSEDRs2aPQ6hJCqU+fx6O3RSejn6/JawzMaPxb+7rvAgQP84S6/QVjQbzJ8tsbxyVdMYiaGOgATdl5DcKemmD/IF0uOxmNHTBJ6eDthXbAf7jzNQadmDuA4DguH+CKotQv8PewpmSGkjlE7oXn33XeRlZWF0aNHIyIiAj179uR7Zp4/f46oqCi4urpq9OLq/EVXr149rF+/HuvXr9eobkKIdiiGZy48KNk3ycfVhk8ajDh5j8brDM8oNpqMup+B0C6eSomHoLfk1SvAQTj5ePiYNbjZSL4qcNn9nBTkWxm84M9feJCBsPAb+CkkgB8u4ziuTs2ZIYSU0Gil4IkTJ2LhwoUYNmwYzp07hwEDBuDZs2c4e/YsGjVqVF0xEkJ0yNzEGD+FBFTb8Iw6u2ZP2hUHUbEUOHJEKZn5Zl8cn8xUpmyyE3U/A9eTs6oUNyGkdtH4Kac5c+bg5cuX6N27Nzw9PXHu3DnBk0mEEP2jSGquJ2dpfXhGsWt2aaoSj4I+/WEedbrkolmzgG+/xVcyhuineSrn+NS3MAWgemIwIE/OOjWr+UfNCSHap3ZCU3b/JFNTUzRo0ADTpk0TlB88eFA7kRFCahVzE2PBcIy2hmcUu2aXN0+nfkE2bvz4obDw2jUgIKDSCcuvCstfaVTnT2cRQrRK7YTGrsyTBKNHj9Z6MIQQw6N4LPzy4xdKiUn/e9HY9PsK4Q0iEWBmBkC9CcvlUTydtWAwJTWE6AO1E5rt27dXZxyEEANVXi/L3vAv0enJnZLrvvoK3LJlgmsqm7BcGW08nUUIqR00mhRMCCHaVraXpUH+KyStGixIZvqH/ojY8TOV7q1swrJ8Dk0JWjyPEP1FCQ0hRKdKr9o7NP4crq0bw58rMDWH15zDaNj1rXITD0VS8+vHnbBgsC+MjOQTln/9uBOOT+/GX0eL5xGi32rN5pSEEMNQdgE9M2MjjO/igQVLQ9E8KZ6/7syHUzG+SZDyOjTl1MHAIJbKYG5izE9Ylkjkk4K3je3A7zVFi+cRop8ooSGE1BhVC+it3XkWs8b3Fl4YH4+3W7XCr49eKiUeGi3C96+3mjrQ4nmE6DkaciKEvBZRsRQxiZmCvZJiEjPlC+GVua7sAnrffzRPkMzkWttBVCQCfHz4xENVMqPWInyEEIOiVg/NH3/8oXaFpfd5IoToN016SwQL6DGGk9umoGVmCl/Xsp7jLIp3+wAAIABJREFUsaXjCHx95R+MCfRQORyk7iJ815OzqBeGEAOjVkIzfPhwtSrjOA5SKf1lRIghUNVbUnavpEm74vikRrGA3qkTVxCzcbygrp4fb0aSQ2P4uNrgm4gEXHiQqXLoqLJF+ABa/ZcQQ6XWkJNMJlPri5IZQvSTqmGlXZeSEHU/Q7ABpKrekt2XksEYky+g90+UIJn5x9YJTef8gSSHxjA3MVJKhsoOHSkW4Sv9+HVptPovIYaL5tAQQiqk6IkJ3nIZS47GQyZjWHwkHssi/kZje4sK721sXw/fRCRgyR93wJo3h9GUKfy5BX0+QdfJ28E4o39fR8afK2/jyMq2OlCs/qtIvAghhqNKTznl5+cjKioKKSkpEIvFgnOff/65VgIjhOieqmGlS4kv8HeaPKF4llUIKzNj5IuVe2etzI2Rml0Et6w0LBw+WHCu66db8Y9dwwpfW9XQkTpbHdDqv4QYJo0Tmhs3bmDgwIEoKChAfn4+HBwckJmZCUtLSzg7O1NCQ4geUTUJV5HMAAADVCYzAJAvkmL81cNYcGYLX5bo0AS9J24E/h0SMjXiIJEp96aUN3RU2VYHRpx8wTxa/ZcQw6PxkNOMGTMwZMgQvHr1ChYWFoiNjUVycjICAgLw7bffVkeMhJBqVt6j1/7u9vzKupowkklxdd1HgmTmy6Aw9P54E5/M1Lc0VZnMAOUPHVW21QGt/kuI4dK4h+bmzZvYvHkzjIyMYGxsDJFIhGbNmmH16tUYO3YsRowYUR1xEkKqSWWPXm/6yF/lTtjlafbiH5z5+VNBWafJO5Bm24A/drYxR3quqMJ6yhs6UiQ115Oz0KmZA63+SwgBUIWExtTUFEZG8o4dZ2dnpKSkwMfHB3Z2dnjy5InWAySEVB91Hr3u/V0UnmUVqVXf5Nh9mBu1kz++07A5Bo/9nu+VUUjPFaGxvQWeZRVC0QejydCRuYmxINGh1X8JIRonNH5+frh69Sq8vLzQo0cPLFiwAJmZmdi9ezfatGlTHTESQqqJOgvVqZPMGMukuPnDB7ARF/JlMwbNxKE2vcq952lWIdo3scNf/2Qr9QzR0BEhRFMaJzTLly9Hbq78F96yZcsQEhKCyZMnw8vLC9u2bdN6gISQ6qPOQnWOVmZ4kS8u97x3RhJObQsTlHWYsgsFDk7gxNIKe2DWBfvhztMcGjoihLw2jROaN998k/+3s7MzTpw4odWACCE1R7FQXeyjF4Knl0p7kS+GlZkR8sUypXPTL+7B9Ohf+eOrjX3x3oer5ENMYika21vgaVZhhT0wNHRECNEG2m2bEAPGGMPCI3fLTWYU8sUyGBsB0n9zGhNpMRK+GwlTWckj22FD5+CoT3fBfU+zCjF/kA/Gd21KPTCEkGqlcULTtGnTCpcVf/To0WsFRAipGZcfv4CRkQl2X0pW63pFMuP7/BGO7RCuN+U/dQ9eWtoJyhTDSh8FevC/M6gHhhBSXTROaKZPny44lkgkuHHjBk6cOIHZs2drLTBCSPUQ/7s/0oSd1zDqLU+0a2yHW0+z1bp3TtQOfBa7nz++4PEGxnzwjeAaxVwZmthLCKlJGic006ZNU1m+fv16XLt27bUDIoRUH1GxFJ/vvYmh/+4osOtSMrwbWlV6n1mxBPfXvCMom/TO/+GUd6CgzK2+BY6GdcWVpFc0rEQIqVFa25xywIABOHDggLaqI4RoSelVgOOSX+Hiw0zB+fvP8yu8v13qfaVkpv3nvyolMwDw5FUhvjmWgE7NHCiZIYTUKK0lNPv374eDg0PlFxJCaoyoWIqJO64heMtlLD5yFx09HeDtbK32/QtO/4Q/ds3kjyNbdITn3KPItrAp9x754nwvXytuQgjRVJUW1is9KZgxhrS0NGRkZGDDhg1aDY4QUnWKZObCvz0yO2KSEfvoBR5n5AGeFd9rLhHh3ncjBWXj3l2Ic807VHgfB6C7N20OSQipeRonNMOGDRMkNEZGRnByckLPnj3RqlUrrQZHCKm62Ecv+GRG4e+0PJhXMhIU8E88DuyZIyhrO/1/yDWvfK5N1xaOKicCi4qliEt+hcBmjuA4DowxXHr0AgEe9WloihCiFRonNIsWLaqGMAghWqd6I+sKLT+xDsF/lSyWeaRVN0wdNlft+yd2a4a45FeCRKWyzS/pSShCiDZonNAYGxsjNTUVzs7OgvIXL17A2dkZUqm0nDsJITXJ1Fj9KXIW4iIkrH1XUPbhqG8Q7fmG2nV092qA03+nY/elZD5RAVDp5peTdsVRUkMIeW0aTwpmTPWffSKRCGZmZhrVdf78eQwZMgSNGjUCx3H4/fffBefHjRsHjuMEX/3799c0ZEIMkp+7HazMKk8SOqXcUkpmWk//TaNkBpDvoK1YpE+RqMQ+eoGo+xmQlfq1UXbzy6j7GbienKXRaxFCSFlq99D897//Bf6/vTsPj+lq/AD+nWwjIRFZSEIkEYTYSkjELiKJN9UQXrXU2qKWF0Ut/ZXaimqraJW29rf2tUUtoRVLYpfXnkYklgoqlmwymZnc3x9phpGZzExMzPb9PI/ncc8999xzcqhv73ZQ9KXPFStWoFKlF29KyOVyHD16VOdnaHJzc9GkSRMMGTIEsbGxKutERUVh9erVim2xWKzTOYjMnbrnU/535wlyC0q/Yvr1nq/R48ofiu2tDcPxcXTRxzO9q9ijfV13/HzqttIxNV3scfvxc7zq5eUTioPKh+1raVz8cnBrX7SsxTckiej1aB1ovvnmGwBFV2iWL18Oa+sX/+dnZ2cHX19fLF++XKeTd+nSBV26dCm1jlgshoeHh07tElmKV59PmRQZgHGbk3DgygM08FT/erbN8+dInttHqaxX3/k47d1QsX3nyXPYWIuwdkgLrDiahmM3HimegZm5+wrWalgyoSiouCLEzxWn0jKVrswUq+/piGnRgaUup0JEpA2tA01aWhoAoGPHjtixYweqVKlSbp162ZEjR1C1alVUqVIFYWFhmDNnDlxduRYMUXGYOfrni+dTtpy9g1xJ0VWZKxk5Ko9rffM8oud+plRWb/w25NtWKFF3TcItRDbwxIpBzXH+1lO0rOUCkUiEGe80QEQDD8zec1XlwpbFQQUAZu25qjLMAEW3n2bvvYrpbzPUENHr0fmh4D/++ENzJT2JiopCbGws/Pz8kJqaik8++QRdunRBYmKi0hWil0kkEkgkEsV2VlYWgKI1p6RSqd76VtyWPts0VpYyVlMZZ4FMjgt3ngICcDL1IV5+TEYmk5X6Wvbi7fMQlZyg2F7f7F+YFTUCACB+5bUoKxHQyt8Njb0qwkooRPOaTpDJZACKrtQevpKBtL+zVJ7v5sMsfL7nMjoEuGHjqbRS+7ThZBrCA9wQ7Kf/206mMqevi+M0P5Yy1tLGqevYRYK6p3zV6NGjB4KDgzF5svKrnAsWLMCZM2ewdetWnTqg6IhIhJ07d6Jbt25q69y8eRP+/v44dOgQOnXqpLLOjBkzMHPmzBLlGzZsgIODQ5n6RmTqbHJyEP3ee0plR7/4Ak8CAgzUIyKi0uXl5aFv37549uwZnJycNNbXOdC4u7vj999/R6NGjZTKL126hPDwcDx48EC3Hhd3RItAU3z+OXPmYPjw4Sr3q7pC4+3tjUePHmn1A9GWVCpFXFwcOnfuDFtbW721a4wsZazGPs4CmRyjN15AYmqmTp+Y6ZByGj9sna1UtnvLFvzfxQqQFCrf5rESAa1quWJAqC+a+1aBnZpXqQv+WeQyIfWR4g2mgGqOSH6Q/aIdfzcs6V30ptSYTUk4fuMR3gvxwaTIAHyxPxnrT99Cm9pFddSd53UZ+5zqC8dpfixlrKWNMysrC25ubloHGp1vOeXk5Kh8PdvW1lZxe6e83L17F5mZmfD09FRbRywWq3wTytbWtlz+UJRXu8bIUsZqrONMSHuCP/58jKIFBrSzausMhN08q9j+qUU3fNX5fSywk0NSKIJEXtSWdxV73HnyHO3ruuN7Lb4JY2tri+/7tyj1g3nf93/Rzvf9Wyg9gzPtnYbo3NDrja3Ibaxzqm8cp/mxlLGqGqeu49Y50DRq1AibN2/G9OnTlco3bdqEwMBAndrKycnBjRs3FNtpaWlISkqCi4sLXFxcMHPmTPTo0QMeHh5ITU3FpEmTULt2bURGRurabSKTJpHJ8VP8Ta3rOz/PQtKSvkplbw9chMsetUs8KwMUvdE0Lbo+3gv10TpgiG2s8eOAIKWg8lnXQEQ28CgRVMQ21gj1f/Ewv0gkUtomInpdOgeaadOmITY2FqmpqQgLCwMAHD58GBs3btT5+ZmzZ8+iY8eOiu3x44tW9R04cCCWLVuGixcvYu3atXj69Cm8vLwQERGB2bNn81s0ZHFO3szE8dRMrepGJZ/A8l3zlMrqTNwJqbX6/9up4mCLXi28db5awqBCRMZC50DTtWtX7Nq1C3PnzsW2bdtgb2+Pxo0b49ChQ2jfvr1ObXXo0EHtl4cB4MCBA7p2j8gs2Vpp91HvDRs/QavbFxXb34X2wlftBmg87kmeFJf/ymIYISKTpXOgAYDo6GhER0eXKL98+TIaNmyo4ggieh2h/q6IDKyGA1dVP3TvmvsU575Tfoupy+AluFa1llbt82u9RGTqdF7L6VXZ2dn48ccfERwcjCZNmuijT0SEoudmDl27jx+PpuJ5gRT5BTKV9d65Gq8UZiTWtqg9cVeJMCO2Uf3XnV/rJSJzUKYrNEDRwpIrVqzAjh074OXlhdjYWCxdulSffSOyWBKZHENWn8GJf56bWfDbdZSIM4KAnf+diKYZyYqir9v0w7et+7xaU+36SwC/1ktE5kGnQHP//n2sWbMGK1euRFZWFnr16gWJRIJdu3bp/IYTEb3w8gKTBfJCDF51Ggk3Hyv2vxpmqmZn4vT3A5XKOr2/DKlu3irbVxdmiq0+kY6IQA8+Q0NEJkvrW05du3ZFQEAALl68iEWLFuHevXv49ttvy7NvRBaheE2mvj+dwoj153Em7bFSmHlVz0uHlMLMkwqOqPXxL2rDTDF1116sRED7uu5o5uNclu4TERkFra/Q7Nu3D2PGjMGIESNQp06d8uwTkcUoDjPHUooWmNx/+T5O3VTzerYgYP+q0aj36MUq13M7DMaPIT00nmdAqA9uPcpFfErR13oh3ES/YB+sSryNtnXc8aMWH9MjIjJmWgea48ePY+XKlQgKCkL9+vXRv39/9O7duzz7RmS2im8x5UlkiP9ntexiT/JKLsjmlfUQCcuGKJV1HPoD0lyqazxXfU9HzOjaANLCQpy/9RRB3o7Yt+8mpnQJeKNf6yUiKk9a33Jq2bIlfvrpJ2RkZGD48OHYtGkTvLy8UFhYiLi4OGRnZ5dnP4nMxsu3mEZtuKCxft+kfUph5p6jG/wm/apVmAFePPRrZ22FUH9XxYO/xR/BY5ghInOg82vbFStWxJAhQ3D8+HFcunQJEyZMwPz581G1alW888475dFHIpMmkcmRkPoIgiBAIpPj/dVnFFdlJLJCtceJhELE//AB5h548fbgZ+HD0WrkGggi3f7qrj6RjpOlPJdDRGTqXus7NAEBAViwYAHu3r2LjRs36qtPRGbj5asxs/ZcRfyfD7VawsD76X2kLXgHPk/vK8raDF+BtUFdtTpvfU9Hxe/50C8RWYLX/rAeAFhbW6Nbt2749ddf9dEckVkoDjNH/7kas/pEOoavO6/xuEFnf8WxHz5QbN+s4gXfSbtx19lD63P/37/qY1ArXwDgQ79EZBHK/GE9IirduVtPSjzwq37lsqJbTCe/H4RqOS9uDU2NHI2Nb0XpfO5CQVC78jURkTlioCEqJ81qOsPLuQLuPc3XWNfv8V/446fhSmWhI1Yjw8ld5/O29ndFSC1XrnxNRBZFL7eciEiZRCbHmoR0rcLM8FPblMLMlaq14Dtpd5nDzKrBLXhFhogsDq/QEOlZ8bMzr95uepV1oRwXlvSFkyRXUTbhXx9he6NOOp9zSmQAAqs7IaQWX8MmIsvEQEOkR9n5UvRbcQoX7z4rtV6dv28hbtUopbLgkWvx0FHzLaK61SrizwdFIUgEoE1tNwxu68cgQ0QWjbeciPREIpOj708nNYaZMSc2KoWZs9Xrw3fSbq3CDAB81rWh4g2mdnXdsWJQc4YZIrJ4vEJD9JokMjlO3szEj/GpuPRXltp6NnIZri3sAdtCuaLsP10/xu7A9mqPEeHFm1EiAG3ruKG5bxW08nflG0xERC9hoCEqo+Igs/JYGo6mPCq1bv2HN7Fv9RilsqDRPyOzYukfuxMARDXwwP4r99GurvL3ZPgGExHRCww0RGUgkcnxwdqzOKYhyADAx/FrMerkVsX2cZ8meK/351qdp31ddyzq3QQXbvvyagwRUSkYaIh0JJHJMWT1GZzQsISBnUyKP7/urlQ2vPsnOFC3lVbnaVvbTXFFhldjiIhKx0BDpKM/rj/UGGYaZaRg97qPlMreGrMBT+2dNLbvbG+Lr3s1Rps67rwiQ0SkJQYaIh1k5kgwan3p6zFNO/wT3j/7i2L7kH8LfNDzM63ab+jlhI3DWsKxgu1r9ZOIyNIw0BBpSSKTI/b745CrWZBJLCtA8texSmWDe36GP/xbaGy7tb8LhrevjZBaLrwqQ0RUBgw0RFqQyORYdiQFtx6rXsqg2d1r2LH+Y6WyxmM3IatCpVLbrSS2xuLeb/H2EhHRa2KgIVIjO1+KlcdTkfFUgluPcnAy/anKep8f+A79kvYrtvfUa4vRMZM1tl9JbI34jzvCtZJYb30mIrJUDDREKmTmSNDq80OQqLm9BAD2Bfm49k1PpbL3es3Gcb+mGtt3rWiLPyZ2gJO93et2lYiIwEBDVML/7byILefvl1on5PYlbN44VamswbgtyBU7aHWOzFwprtzL5uvYRER6wkBDhKJnZH6/9gAA8Mv/MlC00IBqX+39Bj0vH1Zsb2vYCROjP1JbX5W2ddzQzKf0rwQTEZH2GGjIYhUvXSAIApYfScWF24+xIFh9/YqSPFxZ1Eup7N0+83CqZiOdztu2jhtWDOSCkkRE+sRAQxZJIpPjgzVncezGi6ULxKXkizZpF/DzlmlKZfU/2obndhV0Ou/HEXXwQTt/hhkiIj1joCGLoyrMlOa7XfPxdvJxxfbPb3XBp5GjdD5v29puDDNEROWEgYYszrlbT7QKM075Obi4uLdSWWy/L3G+Rn2tztOmtit8XSvi51O30ba2G1YM4m0mIqLyYmXIkx89ehRdu3aFl5cXRCIRdu3apbRfEARMnz4dnp6esLe3R3h4OFJSUgzUWzIHEpkcUlkhXB1Kz/IdU8+UCDMB47drDDO2//yNal/HDSsHtcDsbg2xcWhLhhkionJm0Cs0ubm5aNKkCYYMGYLY2NgS+xcsWIAlS5Zg7dq18PPzw7Rp0xAZGYmrV6+iQgXdnl0gksjkGLbuHOL//LvUesu2zELYjTOK7RXNYzCn01CN7TuKrXB8chiuZuSgmY+zIsDw1WwiovJn0EDTpUsXdOnSReU+QRCwaNEifPrpp4iJiQEArFu3DtWqVcOuXbvQu3dvlccRqZKdL0W/Fadw8e4ztXWc87IQ062fUlnXAd/gkmcd7c4hKcTVjBwGGCIiAzDaZ2jS0tJw//59hIeHK8oqV66MkJAQJCYmqg00EokEEolEsZ2VlQUAkEqlkEqleutfcVv6bNNYmfpYC2RyDFhxGsn3s9S+yRSenIil2+cqlTWctANSG1uIUcrngl/SprYbGntVNPqfk6nPpy4sZawcp/mxlLGWNk5dxy4SBEG7/1qXM5FIhJ07d6Jbt24AgISEBLRu3Rr37t2Dp6enol6vXr0gEomwefNmle3MmDEDM2fOLFG+YcMGODho9xVXsiytpk2D+6VLiu0/e/TAtf79DdgjIiLKy8tD37598ezZMzg5OWmsb7RXaMpq6tSpGD9+vGI7KysL3t7eiIiI0OoHoi2pVIq4uDh07twZtra2emvXGJnqWB/nFiDmu2N48lymcr9L7lMkLlYOLn988w3GZvpDclr9l4Jf1tDLCWPC6qC5bxXYmchDv6Y6n2VhKWPlOM2PpYy1tHEW32HRltEGGg8PDwDAgwcPlK7QPHjwAG+99Zba48RiMcTikqsX29ralssfivJq1xiZylglMjkOXrmP/2xM+qekZDjpejUe3+7+8sUx1jZoNnEr5vqJIPlbBIlcc6Cp4mCLNe+HwrGC8f9MVDGV+dQHSxkrx2l+LGWsqsap67iNNtD4+fnBw8MDhw8fVgSYrKwsnDp1CiNGjDBw78hYSWRyDFh5CqfSnqiuIAjY8fNENLuXrCha2KYflrTuA7G1AECu9bn+1cgTlcRG+1eIiMiiGPS/xjk5Obhx44ZiOy0tDUlJSXBxcUHNmjUxbtw4zJkzB3Xq1FG8tu3l5aV4zoboZZk5EsR+fxy3Huer3O+e8xhnlg5QKus8ZClS3H3KdL71p27j7cZefKuJiMgIGDTQnD17Fh07dlRsFz/7MnDgQKxZswaTJk1Cbm4uhg0bhqdPn6JNmzbYv38/v0FDJWQ8fY5W839X+z5S7OXDWLj3G8X2M3FFNBuzAXIr7Z97qe/piGsZ2QAAKxHQto47V8wmIjISBg00HTp0QGkvWYlEIsyaNQuzZs16g70iYyKRyXHu1hOE1nKFSCSCIAhIvJmJIJ8qENtYQyKTY9eFO5i8/YrqBgQB+1b/B/X/TlcUzeswCD+E9NR47hqV7eDj5ogTqZkY3NoX06IDMWvPVaxJSEfbOu74cUAQv/5LRGQk+AAAGa2Xv+z7aqBo7e+KYD8X7DiXjltPVH+rwDPrbyQuG6xU1nHoD0hzqa7x3P7u9tg1qi3sbKxw/tZTtKzlApFIhM+6BiKygYfSl4CJiMjwGGjIKBWHmWMpRcsUrD6RjpM3MxW3fE6kZuJEaqba4/sk7ce8A98ptu9XckHoyDUQRJqXLwvyqYzVQ1qpXLpAJBLxmRkiIiPEQENG6dytJyXWXCoOM6USBPzx0zD4PclQFM3sNBSrm8eUepiDDfB2Y08Ad/FT/xa8+kJEZGIYaMgohdZyRf+WPvjvyVtaH1Pj6X0c/+EDpbK2w1fgjrNHqcfZ21rh2OQwOImt8Ntvd03mA3lERPSC5uvvRAZQIC/ErcxcresPPLdbKcykVfGE36RfNYaZimJrnP6/cLhWKvkxRiIiMh28QkNG6Vz6ExxNeaSxnkgoRML3g+GZ8+J5mk8iR2HDW6pXcX/ZeyHemNylvsl+6ZeIiF5goCGjVCDX/MVe38d/4chPw5XKWo1YhXtOVTUeOzemPvqG1ipz/4iIyLjwlhO9cRKZHAmpjxTfIBIEAQmpjyCRyRX7D16+X2obw05tVwoz19x94Ttpt1ZhBgBquFYqY++JiMgY8QoNvVGlfVumfV13fPqv+ui+9ChyVH9aBlaFclxY0heVJS+er5n4r3HY1ihc6z60reOGkFp89ZqIyJww0FC5efUrv/lSGXr9cBKX7j4DUPLbMvF//l3iVe2X1X50G4dWjlQqCx65Fg8dtQ8nbeu4YcXA5nwtm4jIzDDQULlQdSVm7KYkXPwnzBTT6tsyAP5zYiMmHF+v2D7vFYDY974CRCKtjm/t74rh7f0RUsuFYYaIyAwx0JDeafrKry5s5DJc+aYnxHKZomxM14/xa2B7rdvgVRkiIvPHQEN6V+av/L6i/sOb2Ld6jFJZ0OifkVlR8wrXNlbAt32aoqLYBiG1XBlmiIjMHAMN6V1oLVcMauWLNQnpZW5j4tF1GJ24RbGdULMx+vaZq9Wx/u4VsWV4KD+WR0RkQRhoSO9EIhGmvx2IU2m632aylUuR8lV3pbIPu03F/oDWGo99t5knugX5cCVsIiILxEBDeicIAmbtuapzmGmUkYLd6z5SKntrzAY8tXfS6viIRtW5EjYRkYVioCG9S7yZqfPtpv/7fQWGntml2D7s3wLv9/xM6+NDa7mgTR03nc5JRETmg4GG9C7Ipwra13XH0T//hqChrlhWgOSvY5XKhvSYjt9rB5d6nAhQtB1aywVrhgTzNhMRkQVjoCG9E9tY48cBQfjXknikPnyutl7Tv65j588Tlcoaj92ErAqlL0vgUckGRyZ3woXbTyGVC/y2DBERMdBQ+bh4+2mpYWb2we/R/8Jviu29Aa0xqttUje36uNhhz5gOqGBrg1B/3mIiIqIiDDSkd5tPpWHyzqsq91WQ5uP6wp5KZf17zcIxv2Ya2w3yqYwNQ0N5NYaIiEpgoCG9yXj6HP1XJuDG3/kq9wffuYwtG6YolTUYtwW5YodS261iAyweEMxbS0REpBYDDenFlbvPEP3dcbX7v9y7CP++fEixvb1hGCZEj9fYrlclKxyYGA7HCrZ66ScREZknBhp6LRKZHN8cuI7lx9JV7q8oycOVRb2Uynr3mYuTNRtrbNunii0OTujEqzJERKQRAw2VmUQmR6uZ+5EpVb2/dXoS1m/+VKms/kfb8Nyugsa263lWwtbhrRhmiIhIKww0pBOJTI5zt57AzcEOEYuPqa337S9foOv1F/s3NInCJ1GjtTpHa39XrBrcgmGGiIi0xkBDWiuQyTFy44USK2m/zFGSi0uL3lUq69FvAc7VCNT6PMPb+zPMEBGRThhoSGsj159H/I0navd3SD2DNdtmKpUFjN8Oia32q14Pbu2LtlzCgIiIdMRAQ6WSyOQ4k5YJADiZ9hhFiw6U9NP22eh845Rie1XQO5gVPkync9XzqIRp0YEQiVSfg4iISB0GGlJLIpPjg7Vncfrm31igZmmlys+z8b8lfZTK3hmwEBc96+p8vuv3czB771VMf5uhhoiIdGNl6A6QccrOl6L70hM4lvJIbZ2IPxNLhJm6E3aWKcwUW30iHSdvPi7z8UREZJl4hYZKkMjk6P79cdx4mKe2zn83fYq2t5IQcevKAAAfaklEQVQU29+37IkF7Qe91nmtREDbOu5o5uP8Wu0QEZHlMeorNDNmzIBIJFL6Va9ePUN3y6xJZHJ0/uqQ2jDjkvcM6V+8rRRm/jVoiVZhpoWvM1YNbI52/zz0O6iVDzZ8EIL+LX0AFIWZHwcE8Q0nIiLSmdFfoWnQoAEOHXrxyXwbG6PvskmSyOQ4eTMTkzecwX3VSzGhy9VjWLRrgWK7wMoGgeO3QWZd+pxUEgGLBzRHmzpuENtYo3UdN5y/9RQta7lAJBIh1N8V/2rkiWY+zgwzRERUJkafDmxsbODh4WHobpg1iUyOnsuP49LdHNUVBAFho0cj5u5dRdE3rfticZu+Gtv2rAQcnBihtBaT2MYaof6uiu3iUENERFRWRh9oUlJS4OXlhQoVKiA0NBTz5s1DzZo11daXSCSQSCSK7aysLACAVCqFVKrmG/1lUNyWPts0hAKZHF0WHsKDPECs4uKIz+N7OLh8uFJZ9NDvcMPdB2IIGtuf36MZKlibxs/JXOZUE0sZJ2A5Y+U4zY+ljLW0ceo6dpEgCJr/VTKQffv2IScnBwEBAcjIyMDMmTPx119/4fLly3B0dFR5zIwZMzBz5swS5Rs2bICDg0N5d9msNFi9GrV/+UWp7Nft2yFY87YQERGVr7y8PPTt2xfPnj2Dk5OTxvpGHWhe9fTpU/j4+GDhwoV4//33VdZRdYXG29sbjx490uoHoi2pVIq4uDh07twZtra2mg8oJwUyOS7ceYpg36LnUQRBwOn0x2jq7Qw7Nc+jFMjk2HHuL8zZd011o4KA5HnvKBXdb94cEVHTISks/fswbf1ccSwtE21qu2FJ77fU9sEYGcucljdLGSdgOWPlOM2PpYy1tHFmZWXBzc1N60Bj9LecXubs7Iy6devixo0bauuIxWKIxSU/tW9ra1sufyjKq11tSF5aW2lwa19Miw7ErD1XsSYhHe3rqn5jSCKTo828g3hSAKj66m/tR7dxaOVIpbLeAxagT2xdSE6LIJGrDzSJU8LgUbkCTt58bNIP+BpyTt8kSxknYDlj5TjNj6WMVdU4dR23Ub+2/aqcnBykpqbC09PT0F0xOIlMjmHrzuFYStFCkatPpCP622NYk5AOADiW8jeGrTsHiUyuOCYzR4KAT/f/E2ZKmnNgaYkw4//xL7hQo36pfXG2Ay7NiICns73iAV9TDTNERGSajDrQTJw4EfHx8UhPT0dCQgK6d+8Oa2tr9OnTR/PBZu7crSeI//NvFL50w/BaRrbi94UCEP/n3zh/6ykA4MrdZwiac+jVZgAAIqEQ6V+8jfeS9inKfq3fDr6T90BuVXowGdfRD6emRym9xURERPSmGfUtp7t376JPnz7IzMyEu7s72rRpg5MnT8Ld3d3QXTO40FquGNTKV3FFRpXBrX3RtGZldFl4GNceqv64TOCDm/htzRilsrcHLsJlj9oa+xA/sQN83Crq1G8iIqLyYNSBZtOmTYbugtESiUSY/nYgTqVlKl2ZKVbPwxHt6rqh3rQDatv4es/X6HHlD6Uyv0m/QhCVfuHO0RpImBbBqzJERGQ0jDrQkHqCIGDWnqsqwwwAXL+fjcGrz6rcZ1Uox80vY5TKNjWOwJQuY1TWf9n/RQVgQBs/PiNDRERGhYHGRCXezCz1dpM6Te4l45f/TlAqixzyHZLdfTUee/ij9qjhpvr7P0RERIbEQGOignyqoH1ddxxLefFgcH1PR7VXbABg2c656PJnglKZ76TdgKj0b8sUq1a5Qpn7S0REVJ6M+i0nUk9sY40fBwShbZ2iB6R7NK2uNszYyGVI/+JtpTCzOqgrfCfv0RhmRABa+rnord9ERETlgYHGhIltrPFd36aoXgnYfuEvlXVa3LmMG191UyoL+2A5ZoYPV1n/Ve3quuP7fs1eu69ERETlibecTFh2vhSNZhxUu3/d5mlol35BqUybW0yOAM7MjsSF28/QzMcZVkKhPrpLRERUbhhoTJBEJseGhDTM/C1Z5X5buRQpX3VXKvu+ZU8saD9IY9sNqlfEjhFtIbaxRqi/KwBAKmWgISIi48ZAY2I0XZVpnZ6E9Zs/VSprO3wF7jh7aGx73ZBghNRy4SvZRERkchhoTER2vhQrjqdi8aFUtXW2/fwxmv+lvIK27+Q9Gtv2drLFoUmdGGSIiMhkMdCYgOx8KYJmHISaNSUhlkqQvLCHUtnXbfrh29aa17xq6OWI7SNbM8wQEZFJY6AxcppuMYXdOI1V22cplbUasQr3nKpqbHvdkBYIqcWVsYmIyPQx0BgxiUxeapjZvWYsGj1QvgWlzS0mtwrAiU+jGGSIiMhsMNAYqc2n0jB551WV+8SyAiR/HatUNqfjEKwIjlVZ/2Uhtapg3ZAQhhkiIjIrDDRGyHfKXrX7mv51HTt/nqhU1mLUOvxdSfPXfHmLiYiIzBUDjZEpLczMjFuGgedf7N9XtxVGdP9EY5tTI/wxPKyeXvpHRERkjBhojMTKI8mYvf+Gyn0VpPm4vrCnUtmAf8/E0VpBGtuNG9cOdTy4QjYREZk3BhojUNpVmRZ3LmPrhilKZQ3HbUGO2EFju+nzo1+7b0RERKaAgcbASgszC35bhF6XDim2dzToiPFvT9DY5kdhfhgbEaiX/hEREZkCBpo3TCKT49ytJzh2PQPLjt1WWceh4DmufvNvpbI+veci0aexxvYTp4TB09leL30lIiIyFQw0b5BEJsewdecQ/+ffauu0Sk/ChlfWYgr8aCvy7DSHlEszIuBYwfa1+0lERGRqGGjeEIlMjiGrz+BEaqbaOot//RIx1+IV2xuaROKTqP9obHtQS29MfbsBX8cmIiKLxUDzhoxae1ZtmHGU5OLSoneVynr2+wJnazTQ2C6vyhARETHQlKvi52X6/nRKbZ0OqWexZtsMpbKA8dshsRVrbJ9vMRERERVhoCkn2jwv8+OOOYhIOanYXh3UFTPDh2tsm28xERERKWOgKQcSmRwdF+zHvSzV+ys/z8b/lvRRKovp/zX+5xWgsW2+xURERFQSA005CPh0v9p9nVNO4qcdc5TK6k7YiQIbzc/BJM/hCtlERESqMNDokUQmLzXMrNs8De3SLyi2l4f0wPwOgzW2O7K9D8Z2rs8wQ0REpAYDjZ68s/gQLmZIVO5zyXuG89/2UyqLHrQYV6r5a2yXD/4SERFpxkCjB6UtXxB97RiW/vqFYlsmskL9CdshtdZ8i4lhhoiISDsMNK+p4YwDAEQldwgCtq6fjBZ/XVUULWrdB4va9CtZ9xVrBzVH+3rV9NhLIiIi88ZAU0bTtichVM3LRu45T3BmaX+lsogh3+FPd1+N7fJDeURERLpjoCkD3yl7IbYWEBpccl/3y7/jm70LFds5dvZoMnYT5FalP9BrDSCVt5iIiIjKhIFGR2qflxEE7F47Do0epCqKFrQbgO9De2lsk69jExERvR4rQ3dAG0uXLoWvry8qVKiAkJAQnD592iD9aDdXdZjxyHqE9AVdlcJM2AfLtQoz6fOjGWaIiIhek9EHms2bN2P8+PH47LPPcP78eTRp0gSRkZF4+PDhG+2HRCbHbRVf/u2ZdAAnlw1SbD+sWAW1Pv4FN11raGyTbzERERHph9EHmoULF2Lo0KEYPHgwAgMDsXz5cjg4OGDVqlVvtB/nbj1RLhAEhI0ejc9/+05RNDvsAwSP/i8KNTwvkzgljGGGiIhIj4z6GZqCggKcO3cOU6dOVZRZWVkhPDwciYmJKo+RSCSQSF584C4rq+iyilQqhVQqLXNfmns7IcJbhPh7hQCAwWd3w/HuXcX+8BE/4k4VT4ghlNrO5RmRiv6YiuK+mlKfy4LjND+WMlaO0/xYylhLG6euYxcJglD6v8AGdO/ePVSvXh0JCQkIDQ1VlE+aNAnx8fE4depUiWNmzJiBmTNnlijfsGEDHBwc9Na3amfPouWcOcitVg2Hli0DrIz+YhcREZHJyMvLQ9++ffHs2TM4OTlprG/UV2jKYurUqRg/frxiOysrC97e3oiIiNDqB6KOIAhoNPOgYltsFYzZu3Zh2lkrSM6q+LDeS45+3BEuFe3KfG5Dk0qliIuLQ+fOnWFra77fyOE4zY+ljJXjND+WMtbSxll8h0VbRh1o3NzcYG1tjQcPHiiVP3jwAB4eHiqPEYvFEIvFJcptbW1f6w9FQuojSOQlg4ukUKSyvJg5vZL9uj9DU8Fxmh9LGSvHaX4sZayqxqnruI36PomdnR2CgoJw+PBhRVlhYSEOHz6sdAvqTQjyqQLXkjlJrSoi8wozRERExsyor9AAwPjx4zFw4EA0b94cwcHBWLRoEXJzczF48OA32g+xjTUSpkWh1ez9yFS9qLYC32AiIiJ6s4z6Cg0AvPvuu/jqq68wffp0vPXWW0hKSsL+/ftRrdqbX7yxONS083NWlF36LAJfRHoBAHo382SYISIiMgCjv0IDAKNHj8bo0aMN3Q0ARaFm3fDWkEql+O233yASifBux6Z4t2NTQ3eNiIjIYhn9FRoiIiIiTRhoiIiIyOQx0BAREZHJY6AhIiIik8dAQ0RERCaPgYaIiIhMHgMNERERmTwGGiIiIjJ5DDRERERk8kziS8GvQxAEALovQ66JVCpFXl4esrKyzH4lVEsZK8dpfixlrByn+bGUsZY2zuJ/t4v/HdfE7ANNdnY2AMDb29vAPSEiIiJdZWdno3LlyhrriQRto4+JKiwsxL179+Do6AiRSKS3drOysuDt7Y07d+7AyclJb+0aI0sZK8dpfixlrByn+bGUsZY2TkEQkJ2dDS8vL1hZaX5Cxuyv0FhZWaFGjRrl1r6Tk5NZ/2F7maWMleM0P5YyVo7T/FjKWNWNU5srM8X4UDARERGZPAYaIiIiMnnWM2bMmGHoTpgqa2trdOjQATY2Zn/nzmLGynGaH0sZK8dpfixlrPoap9k/FExERETmj7eciIiIyOQx0BAREZHJY6AhIiIik8dAQ0RERCaPgaaMli5dCl9fX1SoUAEhISE4ffq0obukVzNmzIBIJFL6Va9ePUN3Sy+OHj2Krl27wsvLCyKRCLt27VLaLwgCpk+fDk9PT9jb2yM8PBwpKSkG6m3ZaRrnoEGDSsxxVFSUgXpbdvPmzUOLFi3g6OiIqlWrolu3bkhOTlaqk5+fj1GjRsHV1RWVKlVCjx498ODBAwP1uGy0GWeHDh1KzOmHH35ooB6X3bJly9C4cWPFx9ZCQ0Oxb98+xX5zmE9A8zjNZT5fNX/+fIhEIowbN05Rpo85ZaApg82bN2P8+PH47LPPcP78eTRp0gSRkZF4+PChobumVw0aNEBGRobi1/Hjxw3dJb3Izc1FkyZNsHTpUpX7FyxYgCVLlmD58uU4deoUKlasiMjISOTn57/hnr4eTeMEgKioKKU53rhx4xvsoX7Ex8dj1KhROHnyJOLi4iCVShEREYHc3FxFnY8++gi7d+/G1q1bER8fj3v37iE2NtaAvdadNuMEgKFDhyrN6YIFCwzU47KrUaMG5s+fj3PnzuHs2bMICwtDTEwMrly5AsA85hPQPE7APObzZWfOnMEPP/yAxo0bK5XrZU4F0llwcLAwatQoxbZcLhe8vLyEefPmGbBX+vXZZ58JTZo0MXQ3yh0AYefOnYrtwsJCwcPDQ/jyyy8VZU+fPhXEYrGwceNGQ3RRL14dpyAIwsCBA4WYmBgD9aj8PHz4UAAgxMfHC4JQNH+2trbC1q1bFXWuXbsmABASExMN1c3X9uo4BUEQ2rdvL4wdO9aAvSo/VapUEVasWGG281mseJyCYH7zmZ2dLdSpU0eIi4tTGpu+5pRXaHRUUFCAc+fOITw8XFFmZWWF8PBwJCYmGrBn+peSkgIvLy/UqlUL/fr1w+3btw3dpXKXlpaG+/fvK81v5cqVERISYnbzCwBHjhxB1apVERAQgBEjRiAzM9PQXXptz549AwC4uLgAAM6dOwepVKo0p/Xq1UPNmjVNek5fHWex9evXw83NDQ0bNsTUqVORl5dniO7pjVwux6ZNm5Cbm4vQ0FCznc9Xx1nMnOZz1KhRiI6OVpo7QH9/R83784Pl4NGjR5DL5ahWrZpSebVq1XD9+nUD9Ur/QkJCsGbNGgQEBCAjIwMzZ85E27ZtcfnyZTg6Ohq6e+Xm/v37AKByfov3mYuoqCjExsbCz88Pqamp+OSTT9ClSxckJibC2tra0N0rk8LCQowbNw6tW7dGw4YNARTNqZ2dHZydnZXqmvKcqhonAPTt2xc+Pj7w8vLCxYsXMXnyZCQnJ2PHjh0G7G3ZXLp0CaGhocjPz0elSpWwc+dOBAYGIikpyazmU904AfOaz02bNuH8+fM4c+ZMiX36+jvKQEMqdenSRfH7xo0bIyQkBD4+PtiyZQvef/99A/aM9KV3796K3zdq1AiNGzeGv78/jhw5gk6dOhmwZ2U3atQoXL582Wye91JH3TiHDRum+H2jRo3g6emJTp06ITU1Ff7+/m+6m68lICAASUlJePbsGbZt24aBAwciPj7e0N3SO3XjDAwMNJv5vHPnDsaOHYu4uDhUqFCh3M7DW046cnNzg7W1dYmnrx88eAAPDw8D9ar8OTs7o27durhx44ahu1KuiufQ0uYXAGrVqgU3NzeTnePRo0djz549+OOPP1CjRg1FuYeHBwoKCvD06VOl+qY6p+rGqUpISAgAmOSc2tnZoXbt2ggKCsK8efPQpEkTLF682OzmU904VTHV+Tx37hwePnyIZs2awcbGBjY2NoiPj8eSJUtgY2ODatWq6WVOGWh0ZGdnh6CgIBw+fFhRVlhYiMOHDyvd9zQ3OTk5SE1Nhaenp6G7Uq78/Pzg4eGhNL9ZWVk4deqUWc8vANy9exeZmZkmN8eCIGD06NHYuXMnfv/9d/j5+SntDwoKgq2trdKcJicn4/bt2yY1p5rGqUpSUhIAmNycqlJYWAiJRGI286lO8ThVMdX57NSpEy5duoSkpCTFr+bNm6Nfv36K3+tlTvX8ELNF2LRpkyAWi4U1a9YIV69eFYYNGyY4OzsL9+/fN3TX9GbChAnCkSNHhLS0NOHEiRNCeHi44ObmJjx8+NDQXXtt2dnZwoULF4QLFy4IAISFCxcKFy5cEG7duiUIgiDMnz9fcHZ2Fn755Rfh4sWLQkxMjODn5yc8f/7cwD3XTWnjzM7OFiZOnCgkJiYKaWlpwqFDh4RmzZoJderUEfLz8w3ddZ2MGDFCqFy5snDkyBEhIyND8SsvL09R58MPPxRq1qwp/P7778LZs2eF0NBQITQ01IC91p2mcd64cUOYNWuWcPbsWSEtLU345ZdfhFq1agnt2rUzcM91N2XKFCE+Pl5IS0sTLl68KEyZMkUQiUTCwYMHBUEwj/kUhNLHaU7zqcqrb3DpY04ZaMro22+/FWrWrCnY2dkJwcHBwsmTJw3dJb169913BU9PT8HOzk6oXr268O677wo3btwwdLf04o8//hAAlPg1cOBAQRCKXt2eNm2aUK1aNUEsFgudOnUSkpOTDdvpMihtnHl5eUJERITg7u4u2NraCj4+PsLQoUNNMpSrGiMAYfXq1Yo6z58/F0aOHClUqVJFcHBwELp37y5kZGQYrtNloGmct2/fFtq1aye4uLgIYrFYqF27tvDxxx8Lz549M2zHy2DIkCGCj4+PYGdnJ7i7uwudOnVShBlBMI/5FITSx2lO86nKq4FGH3MqEgRBeI0rSUREREQGx2doiIiIyOQx0BAREZHJY6AhIiIik8dAQ0RERCaPgYaIiIhMHgMNERERmTwGGiIiIjJ5DDREpJNBgwahW7duiu0OHTpg3Lhxb7wfR44cgUgkKrH+izEw1M+EyJIx0BCZgUGDBkEkEkEkEikWu5s1axZkMlm5n3vHjh2YPXu2VnXfdAjx9fXFokWLFNuCIGDixIlwcnLCkSNH3kgfiOjNsDF0B4hIP6KiorB69WpIJBL89ttvGDVqFGxtbTF16tQSdQsKCmBnZ6eX87q4uOilnfIml8sxdOhQxUrVQUFBhu4SEekRr9AQmQmxWAwPDw/4+PhgxIgRCA8Px6+//grgxW2izz//HF5eXggICAAA3LlzB7169YKzszNcXFwQExOD9PR0RZtyuRzjx4+Hs7MzXF1dMWnSJLy6Wsqrt1ckEgkmT54Mb29viMVi1K5dGytXrkR6ejo6duwIAKhSpQpEIhEGDRoEoGiF4Xnz5sHPzw/29vZo0qQJtm3bpnSe3377DXXr1oW9vT06duyo1E9NJBIJ/v3vf+PQoUM4duyY2jBz8OBBVKhQocQVpLFjxyIsLAwAkJmZiT59+qB69epwcHBAo0aNsHHjxlLPLxKJsGvXLqUyZ2dnrFmzRrGtaS6OHDmC4OBgVKxYEc7OzmjdujVu3bql9c+AyNwx0BCZKXt7exQUFCi2Dx8+jOTkZMTFxWHPnj2QSqWIjIyEo6Mjjh07hhMnTqBSpUqIiopSHPf1119jzZo1WLVqFY4fP47Hjx9j586dpZ53wIAB2LhxI5YsWYJr167hhx9+QKVKleDt7Y3t27cDAJKTk5GRkYHFixcDAObNm4d169Zh+fLluHLlCj766CO89957iI+PB1D0j31sbCy6du2KpKQkfPDBB5gyZYpWP4ecnBxER0fj6tWrOHHihCLMqdKpUyc4Ozsr+gkUhbrNmzejX79+AID8/HwEBQVh7969uHz5MoYNG4b+/fvj9OnTWvVHFU1zIZPJ0K1bN7Rv3x4XL15EYmIihg0bBpFIVOZzEpkdfa6eSUSGMXDgQCEmJkYQhKLVwuPi4gSxWCxMnDhRsb9atWqCRCJRHPPf//5XCAgIEAoLCxVlEolEsLe3Fw4cOCAIgiB4enoKCxYsUOyXSqVCjRo1FOcSBOVVc5OTkwUAQlxcnMp+Fq8A/uTJE0VZfn6+4ODgICQkJCjVff/994U+ffoIgiAIU6dOFQIDA5X2T548uURbrypeydjV1VV4+PCh2novGzt2rBAWFqbYPnDggCAWi0s9T3R0tDBhwgTF9qsrCQMQdu7cqXRM5cqVFStla5qLzMxMAYBw5MgRrcZAZIn4DA2RmdizZw8qVaoEqVSKwsJC9O3bFzNmzFDsb9SokdJzM//73/9w48YNODo6KrWTn5+P1NRUPHv2DBkZGQgJCVHss7GxQfPmzUvcdiqWlJQEa2trtG/fXut+37hxA3l5eejcubNSeUFBAZo2bQoAuHbtmlI/ACA0NFSr9iMiInDo0CHMnTsX33zzjcb6/fr1Q8uWLXHv3j14eXlh/fr1iI6OhrOzM4CiKzZz587Fli1b8Ndff6GgoAASiQQODg5a9UcVTXMRERGBQYMGITIyEp07d0Z4eDh69eoFT0/PMp+TyNww0BCZiY4dO2LZsmWws7ODl5cXbGyU/3pXrFhRaTsnJwdBQUFYv359ibbc3d3L1Ad7e3udj8nJyQEA7N27F9WrV1faJxaLy9SPl3Xq1An/+c9/EBMTg8LCQsVtLnVatGgBf39/bNq0CSNGjMDOnTuVnnX58ssvsXjxYixatAiNGjVCxYoVMW7cOKXbe68SiUQlQqBUKlX8Xpu5WL16NcaMGYP9+/dj8+bN+PTTTxEXF4eWLVtq82MgMnsMNERmomLFiqhdu7bW9Zs1a4bNmzejatWqcHJyUlnH09MTp06dQrt27QAAMpkM586dQ7NmzVTWb9SoEQoLCxEfH4/w8PAS+4uvEMnlckVZYGAgxGIxbt++rfbKTv369RUPOBc7efKk5kH+IyIiArt378Y777wDQRCwZMmSUuv369cP69evR40aNWBlZYXo6GjFvhMnTiAmJgbvvfcegKIHmv/8808EBgaqbc/d3R0ZGRmK7ZSUFOTl5Sm2tZkLAGjatCmaNm2KqVOnIjQ0FBs2bGCgIfoHHwomslD9+vWDm5sbYmJicOzYMaSlpeHIkSMYM2YM7t69C6Do7Z758+dj165duH79OkaOHFnqN2R8fX0xcOBADBkyBLt27VK0uWXLFgCAj48PRCIR9uzZg7///hs5OTlwdHTExIkT8dFHH2Ht2rVITU3F+fPn8e2332Lt2rUAgA8//BApKSn4+OOPkZycjA0bNihdNdFGeHg49uzZg5UrV2L06NEafzbnz5/H559/jp49eypdKapTpw7i4uKQkJCAa9euYfjw4Xjw4EGp7YWFheG7777DhQsXcPbsWXz44YewtbVVOl9pc5GWloapU6ciMTERt27dwsGDB5GSkoL69evr9DMgMmcMNEQWysHBAUePHkXNmjURGxuL+vXr4/3330d+fr7iKsGECRPQv39/DBw4EKGhoXB0dET37t1LbXfZsmXo2bMnRo4ciXr16mHo0KHIzc0FAFSvXh0zZ87ElClTUK1aNUWwmD17NqZNm4Z58+ahfv36iIqKwt69e+Hn5wcAqFmzJrZv345du3ahSZMmWL58OebOnavzmMPCwrB3716sWbMGo0aNUvssUO3atREcHIyLFy8q3m4q9umnn6JZs2aIjIxEhw4d4OHhofTlZFW+/vpreHt7o23btujbty8mTpyo9MyNprlwcHDA9evX0aNHD9StWxfDhg3DqFGjMHz4cJ1/BkTmSiSo+xtNREREZCJ4hYaIiIhMHgMNERERmTwGGiIiIjJ5DDRERERk8hhoiIiIyOQx0BAREZHJY6AhIiIik8dAQ0RERCaPgYaIiIhMHgMNERERmTwGGiIiIjJ5DDRERERk8v4fMhfO8/6P4TEAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        },
        "id": "bclS0f5pd_JZ",
        "outputId": "10b732a4-5263-49c2-e744-60ee08af94a4"
      },
      "source": [
        "\n",
        "from sklearn import metrics\n",
        "MAE =metrics.mean_absolute_error(y_test,y_p)\n",
        "MSE = metrics.mean_squared_error(y_test,y_p)\n",
        "RMSE = np.sqrt(MSE)\n",
        "\n",
        "evaluation = pd.DataFrame(data =[MAE*100,MSE*100,RMSE*100], index='MAE(%) MSE(%) RMSE(%)'.split(), columns = ['Evaluation Values'])\n",
        "evaluation"
      ],
      "execution_count": 137,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Evaluation Values</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>MAE(%)</th>\n",
              "      <td>2.155681</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MSE(%)</th>\n",
              "      <td>1.322553</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RMSE(%)</th>\n",
              "      <td>11.500231</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         Evaluation Values\n",
              "MAE(%)            2.155681\n",
              "MSE(%)            1.322553\n",
              "RMSE(%)          11.500231"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 137
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PW328LSier91"
      },
      "source": [
        "# Hands on Excercise 3 : ALS Selection with ML\n",
        "\n",
        "> A Supervised Classification Problem."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NS1M3KfXfuHq"
      },
      "source": [
        "Source Code : https://github.com/Divyanshu-ISM/Machine-Learning-Deep-Learning/blob/main/Machine%20Learning%20for%20ALS.ipynb"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pvXc9BZFeDXr"
      },
      "source": [
        ""
      ],
      "execution_count": 131,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bKTVN65QeGE3"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}